SS symmetry

Review
Comprehensive Survey of Multimedia Steganalysis:
Techniques, Evaluations, and Trends in Future Research
Doaa A. Shehab * and Mohmmed J. Alhaddad

Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
malhaddad@kau.edu.sa
* Correspondence: dshehab@stu.kau.edu.sa

Abstract: During recent years, emerging multimedia processing techniques with information security
services have received a lot of attention. Among those trends are steganography and steganalysis.
Steganography techniques aim to hide the existence of secret messages in an innocent-looking
medium, where the medium before and after embedding looks symmetric. Steganalysis techniques
aim to breach steganography techniques and detect the presence of invisible messages. In the modern
world, digital multimedia such as audio, images, and video became popular and widespread, which
makes them perfect candidates for steganography. Monitoring this huge multimedia while the user
communicates with the outside world is very important for detecting whether there is a hidden
message in any suspicious communication. However, steganalysis has a significant role in many
fields, such as to extract the stego-message, to detect suspicious hidden messages and to evaluate
the robustness of existing steganography techniques. This survey provides the general principles of
hiding secret messages using digital multimedia as well as reviewing the background of steganalysis.
In this survey, the steganalysis is classified based on many points of view for better understanding. In
addition, it provides a deep review and summarizes recent steganalysis approaches and techniques
for audio, images, and video. Finally, the existing shortcomings and future recommendations in this





 field are discussed to present a useful resource for future research.
Citation: Shehab, D.A.; Alhaddad,
M.J. Comprehensive Survey of Keywords: steganalysis; steganography; data hiding; information security
Multimedia Steganalysis: Techniques,
Evaluations, and Trends in Future
Research. Symmetry 2022, 14, 117.
https://doi.org/10.3390/sym14010117 1. Introduction
Academic Editor: Mihai Postolache Digital communication has revolutionized our everyday lives. Robust and secure
communication is in demand to preserve information security. Many existing secure
Received: 8 October 2021
methods have been proposed and applied, but they are still being developed, to make
Accepted: 3 December 2021
these methods more effective in terms of security and performance [1].
Published: 10 January 2022
In general, information security systems are divided into two main categories, which
Publisher’s Note: MDPI stays neutral are cryptography and data hiding. Both categories aim to secure data whilst the difference
with regard to jurisdictional claims in is implied in their techniques. Cryptography uses various data encryption techniques and
published maps and institutional affil- converts secret data into a hash encrypted package, while steganography does not modify
iations. the format of data but depends on hiding the secret data in innocuous-looking data [2].
Despite the popularity of cryptography techniques, as long as a third party knows
about the presence of a secret message, the attacks will continue. Steganography is a new
step in the encryption world due to fast implementation and no need for large software,
Copyright: © 2022 by the authors. besides the complexity of the composition and decomposition process which makes it
Licensee MDPI, Basel, Switzerland. difficult to crack. Hence, cryptography approaches could be more secure by combining
This article is an open access article them with steganography techniques.
distributed under the terms and Steganography is one of the oldest security techniques, going back to the Greek age.
conditions of the Creative Commons
The word ‘Steganography’ is made of two old Greek words, “Stegano” and “Graphy”,
Attribution (CC BY) license (https://
which means “Cover Writing”. Over thousands of years, it was used in different forms such
creativecommons.org/licenses/by/
as wax tables, human skin, astragali, parchment, linguistic syntax, and newspapers [1].
4.0/).

Symmetry 2022, 14, 117. https://doi.org/10.3390/sym14010117 https://www.mdpi.com/journal/symmetry

Symmetry 2022, 14, 117 2 of 26

During the first world war, microdot technology, using waste materials from magazines
was used by the Germans [3]. In World War II, there were many mechanisms utilized to
write secret messages, such as writing open-coded messages, Enigma machines, and using
invisible ink [4]. In Saudi Arabia, a project for secret writing was started at the Abdu-
laziz City of Science and Technology. The project was about translating some old Arabic
manuscripts on secret writing into English. These manuscripts were written 1200 years
ago. Some of them were collected from Germany and Turkey [1,5].
However, the concept of digital steganography is recently emerging in the past two
decades. The evolution of wireless systems and digital multimedia moved steganography
to digital processing. In this regard, many contributions in this field have been proposed
to ensure the security of sensitive information during transmission [6–8]. Despite the
advantages of steganography, unfortunately, most uses of steganography are regarding
illegitimate objectives involving three major areas, which are terrorism, pornography, and
stolen data [9].
The past years have seen many illegitimate uses for steganography, such as in Berlin in
May of 2011, a suspected al-Qaeda member was arrested with a memory card. The German
Federal Criminal Police claimed that the memory card contained more than 100 text files
about the future operations of al-Qaeda. These files were hidden in a pornographic
video [10]. In the same year, Microsoft researchers discovered a new form of the ‘Alureon’
trojan that exploits steganography to be indomitable [11]. In Japan in October 2018, a
spam campaign targeted users to deliver a banking trojan using steganography. The
malicious code was hidden in a normal looking medium to be undetected by signature-
based detection [12].
Given the illegitimate and dangerous usage of steganography in the past, the re-
searchers start investing many efforts in steganalysis to detect and prevent malicious
usage [13–15]. Steganalysis is the art of extracting hidden messages from a stego-file. These
days, steganalysis has become a complex procedure, especially when it deals with an en-
crypted embedded message [16]. Steganalysis plays a significant role in many applications
such as law enforcement, digital forensics, and national security. In the academic and
research field, steganalysis could also be used to evaluate the strength of the proposed
steganography techniques. Figure 1 illustrates the applications of steganalysis techniques.

Figure 1. The applications of steganalysis.

Symmetry 2022, 14, 117 3 of 26

Steganography is about adding additional “confidential message” information to the

regular medium by trying to maintain a non-suspicious looking content by ensuring the
symmetry between the cover- and the stego- medium. Hence, to perform steganalysis,
we need to select and extract some features from the cover/stego medium then analyze
them to detect any changes. In general, there are two methods for steganalysis based
on its application fields, which are the targeted and universal methods. The targeted
method depends on the steganographic algorithm, hence, the main rule of this method
is to analyze the statistical characteristics or “features” of a medium before and after
embedding them using a specific steganography technique. Although this method mostly
leads to accurate results, it is very restricted to specific embedding algorithms and a specific
medium format [17]. In contrast, the steganographic algorithm in the universal method
is unknown. Hence, the techniques that follow this type design a detector regardless of
the steganographic algorithm, which makes it more practical. Due to that, this type is very
widely used, although it is less efficient than the targeted method. The universal method is
also divided into two blind and semi-blind approaches. The semi-blind approach depends
on the cover and stego medium to determine the decision boundaries, while the blind
approach uses only the cover medium for detection [18]. Figure 2 presents the classification
of steganalysis methods.

Figure 2. The General Classification of Steganalysis.

Steganalysis follows a common schema, which is illustrated in Figure 3. It starts with

extracting some features from the input medium, then analyzes and classifies these features
to detect the steganography. There are two types of features—handcrafted and deep
features. In the first type, the well-known features are extracted manually such as statistical
features. Deep features are not specified explicitly, and they are extracted automatically by
Neural Networks or deep autoencoders [18]. After extracting the features, the classification
is performed to distinguish the cover- and stego- medium. The classification could be
performed in three different ways; first by using a statistical strategy such as an empirical
threshold to detect the existence of a secret message. In the second way, specific features
are fed to machine learning to train and learn the model of the cover medium; hence, in the
test phase, it can distinguish the cover and stego-medium. The final method is by using
Neural Networks. Neural Networks could be used not only for feature extraction but also
as a classifier [19]. Finally, the output of steganalysis could be passive detection, which
considers only the detection of the presence of hidden messages. In active detection, more
information related to the length and/or the hidden information is provided [16,18].
The steganalysis of digital media, such as video, audio, and images, is very important
as these are the most popular carrier files that will be analyzed. For this reason, this
survey focuses on the steganalysis algorithms of digital video, image, and audio. The main
contributions of this survey are highlighted in the following subsection.
Symmetry 2022, 14, 117 4 of 26

Figure 3. The Common Schema of Steganalysis.

Contribution of This Survey

There are few surveys in the steganalysis domain compared with other domains in
security. Table 1 provides a summary of the recent existing surveys published in high-
ranking journals and conferences. Most of these surveys have been published recently in
2018–2020, where the image medium receives the biggest attention.
The contribution of this survey is presented in Table 2. This survey provides a
comprehensive overview of the steganalysis for the most popular mediums (image, audio,
video). It should be noticed that the mentioned criteria in Table 2 are chosen only to
highlight the differences between our survey and the other works. The main contributions
of this survey are:
1. Provide a background for steganography and steganalysis in general;
2. Classify the steganalysis techniques based on different aspects;
3. Deep review for the recent state-of-the-art in steganalysis;
4. Provide a comprehensive overview for new interested researchers in steganalysis.

Table 1. Recapitulation of the existing surveys for the steganalysis domain.

Ref Published Year Journal or Conference Mediums Contributions

Provide an overview of steganography for different kinds of
[20] 2015 Int. J. of Information Image
multimedia.
- Review different image steganography and steganalysis
and Communication
techniques.
technology - Explain the way in which each algorithm works.

[18] 2018 IET Signal Processing Audio - Provide a comprehensive review of audio steganalysis.
- Classify the literature into different categories.
- Conduct comparison between different works.

Journal of information - Discuss and present various steganalysis techniques from

[16] 2018 Image
security and earlier ones to state of the art.
applications - Classify the literature into different categories.
[21] 2018 Int. J. Electronic Video - Present an overview of video steganalysis techniques
Security and Digital
- Discuss the challenging and open issues.
Forensics
[22] 2019 IEEE Access Image - Provide a systematic review of DL applied to steganalysis
- Show the evolution of steganalysis in recent years using the
DL techniques.
- Highlight the most significant results and possible future
work.
Symmetry 2022, 14, 117 5 of 26

Table 1. Cont.

Ref Published Year Journal or Conference Mediums Contributions

- Provide an overview of the steganalysis techniques
[23] 2019 Image
considering: the embedding algorithm,
estimation of the secret message payload and stego key
Multimedia Tools
determination.
- Describe and compare various features of the steganalysis
and Applications
techniques.
- Discuss the challenges and future recommendations.
Audio, - Provide an explanation for the vital elements of
[24] 2020 book (Digital media
Video, steganalysis applied to digital media.
steganography) in Image, - Present a review for the existing works based on ML and
Elsevier Text DL over the last 10 years.
- Provide a short discussion for the challenging and open
(ScienceDirect)
issues.
Image, - Provide a basic guide for the research in steganography
[25] 2020 Multimedia Tools
Video and steganalysis domain.
- Mention the applications, dataset, tools and techniques
and Applications
available.
- Discuss the challenges and future recommendations.

[26] 2020 Journal of Real-Time Image - Discuss the impacts of the real-time Image Steganalysis (IS).
Image Processing - Provide a brief overview of the IS based on deep NNs.
- Analyze a practical real-time IS application and prospect
the future issues of real-time IS.
- Present different NNs structures of the existing literature
[27] 2020 book(Digital media Image
from the period 2015–2018
steganography) in - Discussed the memory and time complexity, and practical
Elsevier problems for efficiency.
- Explored the link between some past approaches sharing
(ScienceDirect)
similarities.
- Discuss steganography by deep learning.
[28] 2020 IEEE International Image
Conference on Visual
- Review the preprocessing modules associated with CNN
Communications and
models.
Image Processing
KSII Transactions on
- Analyze current research states from the latest image
[29] 2020 Internet and Information Image
steganography and steganalysis techniques based on DL.
and Systems
- Highlights the strengths and weakness of existing
up-to-date techniques.
- Discuss the challenges and future recommendations.

Science Technology - Provide a bibliometric analysis of digital image

[30] 2020 Image
Libraries steganalysis from 2014 to early 2020.
- Use a mind map approach to analyze the results obtained
from various of aspects like renowned authors, funding
agencies, and affiliations.
- Review the recent research works in DL based digital
[31] 2021 IET Image Processing Image
image steganalysis.
- Introduce the paradigm shift from ML approaches to
employing more promising DL architectures.
- Conduct comparison between different works.
Symmetry 2022, 14, 117 6 of 26

Table 2. The difference between our survey and other steganalysis surveys.

Steganography Steganalysis Available Systematic

Ref Image Audio Video Datasets
Background Classification Tools Review
[20] X x x X X x x x
[24] X X X x X x x x
[16] X x x x X X x x
[18] x X x x X x x x
[21] x x X x X x x x
[22] x x x x x X x X
[23] X x x x X x x x
[25] X x X X X X X x
[26] X x x x x x x x
[27] X x x X x X x x
[28] X x x x X x x x
[29] X x x X X x x x
[30] X x x x X x x X
[31] X x x X X x x x
Our X X X X X X X x

In the remainder of this survey, an overview of steganography and the common

scheme of steganalysis is mentioned in Sections 2 and 3, respectively. In Section 4, the
recent techniques for audio, image, and video steganalysis are reviewed. The database
utilized in the steganlysis domain is presented in Section 5. In Section 6, we provide the
evaluation metrics for steganalysis, followed by the popular digital multimedia steganalysis
tools in Section 7. Finally, the open issues and the main shortcomings are discussed in
Section 8 and the Conclusion of this survey is presented in Section 9.

2. Steganography: An Overview
Assuring the confidentiality of the transferred information is a crucial element. In this
regard, a few techniques have been established to ensure message confidentiality. However,
sometimes, keeping the existence of the message secret is demanded. This shows the
importance of steganography usage.
The common concept of steganography is to hide the communication between two
sides from the eyes of attackers. Hence, concealed communication can be embedded
in an innocuous medium such as computer code, video film, or audio recording. After
exchanging the data, both parties should destroy the cover message to prevent accidental
reuse [32].
To hide data in any medium, embedding and extracting algorithms are required. The
task of the embedding algorithm is to hide secret information within a cover medium. In
this step, a secret key is applied to protect the process of embedding; hence, ensuring that
only those with the secret keyword can access the hidden information. In contrast, the
extracting algorithm is used on a feasibly modified medium and returns the hidden secret
information [32].

2.1. Steganography Categories

Steganography has three main categories: pure steganography, secret key steganogra-
phy, and public-key steganography [32].

2.1.1. Pure Steganography

This type has no requirement for the exchanging of particular secret information
(such as a stego-key). The embedding operation can be demonstrated by the mapping
E : C × M → C. The extracting process can be demonstrated by the mapping D : C → M.
Here, C indicates the set of probable covers and M indicates the set of probable messages;
|C | ≥ | M|. However, since the parties depend only on the assumption that this secret
information is not known by others, this leads to a lack of security.
Symmetry 2022, 14, 117 7 of 26

2.1.2. Secret Key Steganography

Secret key steganography requires a secret key (stego-key) during the communication.
Hence, the sender and receiver should have the secret key to access and read the message.
This results in more robustness and security.

2.1.3. Public Key Steganography

Public key steganography is enhanced by the concept of public-key cryptography. In
this type, a public key and a private key are applied to ensure the security of communication.
The public key is used by the sender through the encoding process. While the private key is
used to decipher the secret message. Although the public key steganography is more robust,
it decreases the size of the secret message to be embedded. This is because the encryption
algorithms increase the size of the message to more than double its original size.

2.2. Steganography Techniques

The embedding process is very significant for hiding the data in digital media. In this
regard, many techniques have been proposed to enhance the performance of embedding.
These techniques could be categorized under several domains.
In this survey, the steganography domains are classified into six categories, although
in some cases, exact classification is not possible. As illustrated in Figure 4, the domains
are: spatial domain, transform domain, vector domain, entropy coding domain, adaptive
domain, and distortion domain [33]. The spatial and transform domains are the most
popular used in the state-of-the-art, where they contain various techniques that deal with
different digital media steganography (image, audio, video), while the vector and entropy
coding contain the techniques that deal with video steganography. Finally, the adaptive
and distortion domains are a special case of spatial and transform domains [1].

Figure 4. The classification of steganography techniques based on embedding domain.

One of the oldest and most used steganography techniques is Least Significant Bits
(LSB), which was used as an example to explain the general steganography scheme [24] in
Figure 5.
Symmetry 2022, 14, 117 8 of 26

Figure 5. Steganography scheme. Example of embedding a data in LSB. Taken from [24].

2.2.1. Spatial Domain

The techniques of this domain change particular information in the digital mediums
which will be invisible to the human eye. There are various spatial domain techniques such
as LSB, Pixel Value Differencing (PVD), Histogram Shifting, Pixel Intensity Modulation,
Echo coding, and so forth [24].

2.2.2. Transform Domain

The opposite of the spatial domain, the embedding in the transform domain is done
in transformed coefficients instead of straight to the intensity values. Some of the existing
transform domain techniques are Discrete Fourier Transform (DFT), Discrete Cosine Trans-
form (DCT) and Discrete Wavelet Transform (DWT), phase coding, Spread Spectrum (SS),
and so forth.

2.2.3. Vector Domain

The techniques of this domain embed the information into the pixels of video frames.
It is utilized for the H.264/AVC and recently HVC Video coding standard. The Motion
Vectors (MVs) technique is applied in both spatial and temporal domains due to the
correlation between the adjoining MVs [34].

2.2.4. Entropy Coding Domain

The techniques of this domain are used to exploit the benefit of the multimedia format
structure [35]. For example, the H.264/AVC video coding standards provided two kinds
of entropy encoding techniques for embedding, which are CAVLC (Context-Adaptive
Variable Length Coding) and CABAC (Context-based Adaptive Binary Arithmetic Coding).
These techniques were recently also used to embed the data in the H.265/HEVC video
coding standard [25].

2.2.5. Adaptive Domain

This is sometimes referred to as “Masking” or “Statistics-aware domain” [25]; techniques
that use statistics are applied to embed the data into a digital medium by changing some
statistical features of the cover. It mostly depends on splitting the cover into blocks or
“regions”. Then, the best regions, which are sometimes called regions-of-interest (ROI), are
determined in order to embed the data [36]. To find ROI, the researchers used statistical
strategies or combined the techniques of other fields such as Ant Colony Optimization
(ACO) [37]. In addition, to enhance the embedding process, some researchers switched
to machine learning techniques [25] such as Support Vector Machine (SVM), Genetic
Algorithm (GA), Fuzzy Logic (FL), Neural Networks (NN).
Symmetry 2022, 14, 117 9 of 26

2.2.6. Distortion Domain

The distortion techniques hide the data using signal distortion in the encoding phase.
Then, in the decoding phase, the deviation is measured from the original cover. Mainly,
this approach intends to reduce the resulting errors produced by embedding and therefore
to minimize the total signal distribution [36]. This domain includes matrix embedding
strategy (MES), Syndrome Trellis Code (STC) [38], the wet paper code [39], and so forth.

3. Steganalysis: A Common Scheme

The aim of steganalysis is to detect hidden data embedded using steganographic
techniques. Steganalysis includes several tasks concerning the hidden data in the digital
medium like predicting the payload used to embed the data, predicting the steganographic
techniques used, and the classification process of whether the files contain hidden data
or not. The classification is one of the most important tasks in steganalysis [24]. The
classification task includes two significant components, the features, and the classifier. The
following subsections describe them in detail.

3.1. Feature Extraction

The art of steganalysis makes a major contribution to the selection of features or
characteristics that might be shown by Stego- and Cover-objects. There are two types
of features which are deep features and handcrafted features, which are sometimes called
“statistical features” or “specific features”. As illustrated in Figure 6, the steganalysis based
on features could be classified into:

3.1.1. Signature Steganalysis

In this type, the features are considered as a unique pattern or signature. Hence, if the
steganographic embedding technique is identified or was popular, it becomes easy to select
and extract frequent special patterns that have been produced, like histogram arrangement,
minimum and maximum intensity range, and so forth. This type is called target or specific,
while the other one is the universal type where the features are identified as a behavioral
pattern regardless of the embedding technique. Some steganography techniques follow
sequentially or linear access of the cover medium unit for embedding [40]. This leads to an
obvious pattern that can be easily detected; for example, a change in the expected JPEG
compression quantization nature [16].

3.1.2. Statistical Steganalysis

Statistical steganalysis is mainly dependent on extracting statistical features and
properties of cover- and stego- mediums. It also includes target and universal methods. The
target methods are developed by studying and analyzing the steganographic embedding
techniques and determining particular statistics features that have been modified as a
consequence of the embedding operation. Therefore, it is important to deeply understand
the embedding techniques to enhance steganalysis accuracy. That will produce another
steganalysis category depending on the embedding domain (LSB matching steganalysis,
LSB embedding steganalysis, Transform domain steganography steganalysis, etc.) [16].
On the other hand, universal statistical steganalysis does not target any special steganog-
raphy techniques. It mainly depends on the concept of learning and training to find out
suitable sensitive statistical features with ‘distinguishing’ capabilities. These features are
then used to build a learning model for machine learning and neural networks [41].

3.1.3. Deep Steganalysis

We named this category deep steganalysis due to the concept of deep features. Re-
cently, neural networks became a trend in both deep learning and classification tasks due
to their accuracy and ability to enable deep understanding to obtain higher robustness and
effectiveness for semantics representation. Deep steganalysis is like universal statistical
steganalysis in terms of not depending on the embedding steganography techniques, but
Symmetry 2022, 14, 117 10 of 26

the difference is that the first one extracts deep features while the last extracts hand-crafted
features, respectively. However, this method is still recent and needs more investigation.

Figure 6. The classification of steganography techniques based on features.

3.2. Classification
After feature extraction, the classification step is performed which generally includes
three methods, statistical strategy method, machine learning method, deep learning
method. The steganalysis techniques start detecting the stego-medium by comparing
the features of the cover and stego mediums in the case of the targeted techniques. The other
way was to use a statistical strategy such as a threshold, so the stego medium is detected
if the extracted features exceed or are below it. Emerging of Artificial Intelligence (AI)
including pattern recognition, machine learning, deep learning, etc., opened the door for
researchers to exploit their advantages in steganalysis. There are many existing techniques
based on machine learning, while deep learning is still a new area in this field. [25]. The
steganalysis techniques based on the classification method would be classified as presented
in the following subsections.

3.2.1. Statistical Strategy-Based Techniques

In this type, the steganalysis techniques rely on statistical methods such as comparing
the result of the detection with an empirical threshold. Hence, after extracting the features
which are commonly statistic features like mean, variance, histogram, etc., an empirical
threshold is used to distinguish the cover-steg-mediums [42,43].

3.2.2. Machine Learning-Based Techniques

There are two methods for machine learning: supervised and unsupervised learning.
Supervised learning is referred also to as “semi-blind”, this method needs the cover- and
stego-medium to build a training model that is used for detection in the test phase. The
most popular classifier under this method is Support Vector Machine (SVM) which was
applied in many steganalysis techniques. The typical scheme of supervised machine
learning classifiers is presented in Figure 7. On another side, unsupervised learning which
is referred also to as “blind”, only needs the cover-medium to detect the stego-medium
using clustering methods such as K means.
Symmetry 2022, 14, 117 11 of 26

Figure 7. The typical scheme of supervised machine learning algorithms.

3.2.3. Deep Learning-Based Techniques

Deep learning is a subfield of machine learning and, recently, the deep learning
concept is applied in steganalysis. Neural Networks (NN) such as Deep Neural Networks
(DNN), Convolution Neural Networks (CNN), etc. are able to automatically extract the
features and detect the stego-mediums. This area is still new where few techniques have
used CNN in the steganalysis domain. Figure 8 present the CNN deep learning framework.
The CNN contains various hierarchical layers such as the conventional layer, pooling layer,
and fully connected layer. The conventional layer contains filters and is responsible for
feature extraction. In the filtering layer, the down-sampling operation is performed to
decrease the learnable parameters. Finally, the final features of the last layer are flattened
and fed to one or more fully connected layers to get the classification as a final output [44].

Figure 8. Basic Architecture of CNN Framework.

4. Literature Review
Digital image steganalysis algorithms focus on the dependencies of inter-pixels, which
is the foundation of natural images. While digital audio steganalysis algorithms are based
on the file’s characteristic aspects such as the audio signal’s distortion measure and its
high-order statistics. Steganalysis algorithms for digital video target the “spatial and
temporal redundancies in the video signals within the individual frames and at inter-frame
level” [45]. In this section, the recent state-of-the-art regarding the steganalysis techniques
for digital video, image, and audio are reviewed. At the end of each section a summary is
provided in Table 3 for audio steganalysis, Table 4 for image steganalysis, and Table 5 for
video steganalysis.
Symmetry 2022, 14, 117 12 of 26

Table 3. Comparison of state-of-the-art in audio steganalysis.

Ref. Year Type of Features Detection Method Database Steganography Technique Advantage Limitation
Detecting low Time consumption for
Hand crafted Statistical Unknown dataset consists of
[46] 2017 Machine learning SVM MP3Stego steganography. embedding-rate in the the feature
(Markov features) 1000 stereo WAV audios
MP3 audios construction phase.
good performance in
Hand crafted Statistical several MP3
General dataset consists of Different steganography
(Multi-scale correlations Machine learning steganography
[47] 2020 10,000 mp3 files with 10 s techniques includes HCM, High dimensionality
measure for QMDCT (ensemble classifier) algorithms, bitrates,
duration. MP3Stego, and EECS
coefficients) duration, and relative
payloads.
The detection accuracy
Two datasets consist of 4169 Different steganography High accuracy detection decreases in the low
Hand crafted Statistical
[48] 2017 Machine learning SVM wave music clips and 1029 techniques includes LSB, SS, against target and embedding rate for
(Markov features)
speech wave files. DCT, and others universal techniques speech datasets in most
of the cases.
The accuracy detection
Different steganography
General datasets consist of for low embedding
Hand crafted Statistical techniques includes High accuracy detection
[49] 2018 Machine learning SVM 2000 WAV files with rate of ratio not sufficient
(LP features) Hide4PGP,S-Tools, for high embedding ratio
44.1 kHz. comparing with high
StegoMagic, and Xiao
embedding
Improving the
A dataset cotains 6300 mono Moderate detection
Deep features using Fully connected Layer LSB matching and STC architecture of CNN to
[50] 2019 WAV files with rate of accuracy for low
CNN (softmax) steganography techniques enhance the detection
16 kHz. embedding rate
performance.
Two datasets cotain 10,000
with 16-kHz rate and (2 s) Different steganography
Deep features using
[51] 2019 Machine learning SVM duration in AAC format, and techniques includes LSB, High detection accuracy High dimensionality
(S-ResNet)
9000 with 44.1 kHz rate and MIN, SIGN, and MP3Stego
(5 s) duration in mp3 format
Symmetry 2022, 14, 117 13 of 26

Table 4. Comparison of state-of-the-art in image steganalysis.

Ref. Year Type of Features Detection Method Database Steganography Technique Advantage Limitation
Hand crafted Statistical High efficiency with Low detection accuracy
UW image database LSB flipping image
[52] 2018 feature (color Machine learning SVM single dimension in case the low
consisting of 1333 images steganography.
correlativity) analysis embedding rates
The extraction
Fast and low
Statistical feature Hand crafted Statistical performance decrease
[53] 2017 BossBase image database SS steganography. computation complexity
(variance) strategy with high embedding
comparing with [54]
distortion.
Different steganography Not taking in to
Hand crafted Statistical
techniques including Simple and high account the frequencies
[55] 2019 feature (histograms of Machine learning SVM DBLST and BIVC database
distortion-based, performance of different SEs
SE)
pattern-based, and others. patterns.
Adapting new and The pre-processing
Different steganography
Hand crafted Statistical effective statistical law steps before features
techniques including
[56] 2018 feature (Zipf’s law in the Machine learning RF UCID database for extracting features in extraction may
spatial-based,
wavelet transform) the wavelet transform produced high
transform-based.
domain execution time.
Different steganography Low computation
Hand crafted Statistical
Machine learning BSD300 dataset contains 150 techniques including complexity, low time
[57] 2019 features from transform Small dataset
CIML JPEG images distortion-based, consumption, and high
and spatial domains)
spatial-based, and others. performance
Spatial UNIversal WAvelet
Deep features from
Fully connected layer BOSSbase dataset contains Relative Distortion High computation
[58] 2018 spatial domain using High detection accuracy
(softmax classifier) 10,000 images (S-UNIWARD) complexity
DRN
steganography.
Spatial UNIversal WAvelet
An ordinary accuracy
Deep features from Relative Distortion Considering the spatial
Fully connected layer BOSSbase dataset contains although the high
[59] 2020 spatial and transform (S-UNIWARD) and Wavelet and transform domains
(softmax classifier) 10,000 images computational
domains using CNN Obtained Weights to increase the accuracy
complexity
steganography.
Symmetry 2022, 14, 117 14 of 26

Table 5. Comparison of state-of-the-arts in Video steganalysis.

Ref. Year Type of Features Detection Method Database Steganography Technique Advantage Limitation
Contains 100 YUV sequences
Hand crafted features Takes into account the
Machine learning (H.264/AVC standard), each
[60] 2017 NPELO (36-dimensional) MV steganography motion characteristic of Uses small datasets
(SVM) sequence has 150 to
+ MVRBR video content.
300 frames
Their experiment
Hand crafted features 284 uncompressed video The detection accuracy limited and can not
Machine learning
[61] 2018 (entropy,motion, and (H.264/AVC standard) from MV steganography does not affect by the bit detect the currently
(SVM)
statistic features) internet rate variations best steganography
methods
Contains 14 YUV sequences Exploit the videos spatial
Hand crafted features
Machine learning (H.264/AVC standard), only and temporal
[62] 2017 (motion intensity and SS steganography Uses small datasets
(SVM) the first 90 frames from each redundancies
texture histograms)
sequences simultaneously
33 videos each contains
80 frames in 720P and
Exploit the videos spatial
Hand crafted features 30 videos each contains Considering as a
Machine learning PU partition modes and temporal
[63] 2019 (statistic change in 50 frames in 1080P (HEVC targeted steganalysis
(SVM) steganography redundancies
distribution of the PU) standard), only the first technique
simultaneously
90 frames from each
sequences
Hand crafted features Blind steganalysis computational
(statistical features of mMchine learning 22 PAL QCIf video dataset technique and can be complexity due to the
[64] 2020 MV steganography
inter-frame and (SVM) (H.264/AVC standard) adjusted to various video high dimensionality of
intra-frame) codec standards. features
Deep features
Fully connected Unknown dataset contains Did not taking into
(steganographic noise MV and Intra Prediction Universal steganalysis
[65] 2020 Layer(softmax (200,000 frames for training account the temporal
residual features) using Mode steganography technique
classifier) and testing domain.
CNN
Deep features Extract deep features Extract the features
Fully connected Layer Xiph Video Test Media
[66] 2020 (512-dimensional) using MV steganography and estimates the from fixed-size block
(softmax classifier) database(HEVC standard)
CNN embedding rate motion estimation
Symmetry 2022, 14, 117 15 of 26

4.1. Audio Steganalysis

The goal of audio steganalysis is to detect any change in a signal due to embedding
data. There are two main domains for embedding the data, either use a spatial or sometimes
a “time” or “temporal” domain and that mostly happened by changing the least significant
bit (LSB) of a data sample in the audio file, or in the transform domain by modifying
different parameters of the signal. In the addition, the audio steganalysis is classified based
on format into steganalysis techniques for compressed formats such as MP3 and AAC,
and steganalysis techniques for non-compressed formats [24]. Regarding the compressed
formats, Jin et al. [48] proposed a target steganalysis technique for detecting MP3Stego
steganography. The authors noticed that the MP3Stego alters the quantized modified
discrete cosine transform QMDCT coefficients during compression which impacts the
correlations between neighboring QMDCTs of audio cover. Therefore, Markov features are
extracted from cover and stego audio to describe the correlations of the QMDCTs. These
features are then crossed through pre-processing steps to select the optimal features to train
an SVM classifier. According to the experiments, the proposed technique achieves high
detection accuracy in the case of a low embedding rate.
Another steganalysis technique for Mp3 is proposed by Wang et al. [47], where the
QMDCT coefficient matrix of MP3 is calculated to extract the steganalytic features. The
rich high-pass filtering was applied to increase the sensitiveness of their technique against
noise signals. The authors claimed that the replacement of one QMDCT coefficient results
in the changing of one Huffman codeword. For this reason, they suggested a correlations
measure module to detect any possible modification in the QMDCT coefficients matrix at
pointwise, 2 × 2 block-wise, and 4 × 4 block-wise, separately. To reduce the dimension
of the features and select the optimal one, an empirical threshold was applied. For the
classification task, the ensemble classifier [67] was trained.
For non-compressed formats, it includes two methods: the collaborated method and
the non-collaborated method. In the first method, the techniques depend on the compar-
ison between the estimated cover signal and the stego signal. There are many ways to
estimate the cover including denoising based, liner bases of cover, re-embedding, and
others. However, the estimation of the stego signal for calibration is also possible, which is
applied by Ghasemzadeh et al. [46], where the authors proposed a universal steganalysis
technique based on calibration. In their technique, the re-embedding method was used
to embed the signal with a random message. The energy features were extracted, where
each signal and re-embedded signal are segmented into many chunks and calculated the
energy for each. Then, the energy of each chunk from the signal and its re-embedded
counterpart is subtracted. Finally, the statistical properties of the energy features, including
mean, skewness, standard deviation, and kurtosis, are selected to train the SVM classifier.
Their technique has been evaluated using a wide range of various steganography tech-
niques. The experimental results showed its effectiveness in detection in the targeted and
universal cases.
On the other hand, the non-collaborated method extracts the features directly from the
audio signal according to the embedding feature domain. Han et al. [49] suggested a linear
prediction method, where linear prediction LP features are extracted from the segmented
audio file. According to the experiments, the authors found that the LP can significantly
distinguish between the cover and the stego. Therefore, the LP coefficients, LP residual,
LP spectrum, and LP cepstrum coefficients features are extracted from the time domain
and the frequency domain. The SVM classifier was trained based on the extracted features
from cover- and stego-signals. A wide range of experiments are conducted with various
ratio embedding and are tested against different steganography techniques. The results
proved the effectiveness of the proposed techniques compared with popular and recent
steganalysis techniques, where above 96% accuracy is achieved.
Recently, deep learning has attracted more attention and has achieved superior results
in the steganalysis field. Lin et al [50] proposed an improved method based on CNN to
detect the audio steganalysis in the time domain. At first, a High-Pass Filter layer is used
Symmetry 2022, 14, 117 16 of 26

to extract the residual signal from the input audio. Then the hierarchical representations of
the input are obtained using six various sets of layers, where the first set contains only the
activation of the first convolutional layer and the remaining sets contain a convolutional
layer and a pooling layer. After each convolution operation, the non-linear activation
is applied. By the end of these layers, the audio signal is transformed into 215-features.
To detect the steganography, the extracted features are fed into the binary classifier that
contains a softmax layer and a fully connected layer. This approach proved its effectiveness
at detecting different embedding rates.
Ren et al. [51] proposed a universal steganalysis technique where a ResNet is applied
for the features extraction. The spectrogram of the audio signal was used as input for
the neural network, called the Spectrogram Deep Residual Network (S-ResNet). Figure 9
illustrates how the spectrogram can represent the energy information of various frequency
bands over time as well as consisting of valuable time-frequency information in the audio
signal. For this reason, the authors attempted to use it for capturing relative features
produced by the audio steganography technique. The architecture of S-ResNet contains
31 convolutional layers between the batch normalization and ReLU layers, for accelerating
the learning process and learning more complex patterns, respectively. After each group
of convolutional layers, there is a residual unit to compute a residual function. Two
average pooling layers are applied to decrease the data size after every five residual blocks.
Finally, a global average pooling layer is applied to produce the feature vector. After the
S-ResNe trained an efficient model, this model is fed to an SVM for final training and
binary classification. The experiment’s results show superior detection, where the average
accuracy is 94.98% and 99.93% for both AAC and MP3 formats, respectively.

Figure 9. Wave-form and spectrogram representation for an audio segment [51].

4.2. Image Steganalysis

The history of image steganalysis starts in the late twentieth century, with the first
studies proposed by Johnson and Jajodia [68] and Chandramouli et al. [69]. Image steganal-
ysis has cut a long way starting with visual steganalysis and manual features extraction up
to use deep learning and automatic feature extraction.
Symmetry 2022, 14, 117 17 of 26

In the beginning, the researchers tried to find a signature or pattern to detect specific
well-known steganographic techniques [68]; however, this type has only limited applica-
tions. With the evolution and variety of steganography techniques, robust steganalysis
techniques became more necessary. Many steganalysis techniques started to extract sta-
tistical features that can reflect invisible changes in the digital medium. As an example,
Chaeikar et al. [52] proposed a blind statistical steganalysis technique for detecting the
Least Significant Bit (LSB) flipping image steganography. The authors found that the
natural color harmony of the pixel is affected when embedding the data. Hence, a statistical
feature that analyses the color correlativity is extracted from the image pixels to detect the
existence of the secret message. At first, the pixels were classified into three classes depend-
ing on the color similarity with the neighboring pixels, and the level of suspiciousness of
pixels was identified according to the mean and standard deviation. That leads to a dataset
used to train SVM for detecting and estimating the embedded message length.
Another blind image steganalysis is proposed by Soltanian and Ghaemmaghami [53]
to detect the spread spectrum steganography. The core of their method is to discover
the carrier and stego message matrices using a well-known least-squares method. The
carrier matrix is randomly initialized, then the carrier and message matrices are updated
based on a univariate gradient descent method. Their technique is based on the work of
Li et al. [54], where the aim is to reduce the computation complexity and to rely on no
prior knowledge about the number of spread spectrum carriers. Therefore, the proposed
technique consecutively extracts the data bits of each carrier by extracting the variance to
reduce the computational cost. To detect and estimate the number of embedded messages
without prior knowledge, the proposed technique intends to reach the disturbance of the
residual stego-image to a minimum by reducing the variance of the residual stego-image.
A statistical model based on a histogram of pixel structuring elements is proposed
by Lu et al. [55]. This model is developed to extract the steganography in binary images,
where the image contains only two values (0 and 1) unlike color and grayscale images. The
histograms are computed for all structure elements (SE) in the image; Figure 10 represents
how large SE can contain several neighboring small SEs. Then, only the bins of SEs that
have a high probability of flippable pixels are selected as a feature set using an empirical
threshold. The SVM classifier is used to detect the stego-image. In addition, the authors
create available datasets for binary images called DBLST. The DBLST and the open BIVC
dataset are used for experiments which show that the proposed technique outperforms the
state-of-the-art techniques in detecting different types of stego images.

Figure 10. A large SE can be considered a union of various neighboring small SEs [55].

Laimeche et al. [56] proposed a universal steganalysis technique, where Zipf’s law [70]
is exploited to extract the features in the wavelet transform. The basic idea of Zipf’s law in
image representation includes three phases. The first phase is a mask size for counting the
frequency of patterns appearance. The second phase is to minimize the number of patterns
by identifying significant wavelet coefficients, this leads to a more significant distribution
for pattern frequency. In the third phase, the Zipf curve, is produced, which represents
the pattern frequency and the number of pattern axes. Finally, Area under the Curve of
Symmetry 2022, 14, 117 18 of 26

Zipf, Inflection Point, and Subband Auto-Similarity Metric) features are extracted from the
produced Zipf curve. To detect the stego images, the random forest classifier is trained
using the UCID dataset.
A novel steganalysis technique that aims to reduce the computation and time con-
sumption along with high performance is proposed by Guttikonda and Sridevi [57]. Each
Coefficient based Walsh Hadamard Transform and Gray Level Co-occurrence Matrix is
used to extract the features from the transform and spatial domains, respectively. To
reduce the feature dimensionality and select the most relevant features, the Pine Growth
Optimization algorithm was applied. Finally, the selected features are used to train the
Cross Integrated Machine Learning classifier to distinguish the cover- and stego-images.
The experiment’s results showed the effectiveness of the proposed technique in terms
of detection accuracy and the execution time, where it reduced the time by about 0.66
compared with the existing Multi-SVM technique.
Very deep learning and automatic feature extraction are applied in the work of
Wu et al. [58]. Specifically, a novel CNN model called Deep Residual learning Network
(DRN) is proposed for image steganalysis. The authors have proved that the very deep
neural network that contains many layers can reflect complex statistical properties, which
leads to more effective distinguishing the stego-images. The main idea of their technique is
to feed the network with noise components of the image, instead of the original image to
force the network to consider the weak signal produced by data embedding. Thereafter,
DRN is trained to learn the effective features of cover- and stego-images. For the binary
classification, a fully connected layer with a softmax classifier was performed. The ex-
perimental results conducted using the BOSSbase dataset showed the superiority of the
proposed technique compared with other deep neural networks-based techniques.
Another deep neural network-based technique that extracts features from multi-
domains is proposed by Wang et al. [59]. Firstly, two famous steganalysis methods are
simulated which are spatial rich model SRM and DCT residual for detecting the steganog-
raphy features in both spatial and transform domains. In the next step, the previous linear
features with nonlinear SRM features are fed to the CNN layer to extract general fea-
tures. Finally, the fully connected layer is applied for stego- and cover-image classification.
Through the experiments, the authors proved the effectiveness of considering the nonlinear
features extraction as well as extracting features from multi-domains, where the detection
accuracy is increased by 0.3~6% and 2~3%, respectively.

4.3. Video Steganalysis

The rapidity of the internet led to the wide usage of videos. Videos can be altered
to send hidden messages, therefore detecting these changes are necessary. At first, the
steganalysis techniques for images were straight utilized to detect the changes that pro-
duced from the embedded message. But since there is not change much between the
successive frames in the video, these approaches did not produce good results. There-
fore, there are significant differences between image steganalysis techniques and video
steganalysis techniques. There are two main methods to detect the hidden messages in dig-
ital video, which are methods-based motion vectors field and methods-based inter-frame
level. These methods have been utilized in videos in H.264/AVC standard and, newly, in
HEVC standard.
Wang et al. [60] proposed a steganalytical technique based on motion vectors, taking
the advantages of content variety. The video is divided into subclasses; each class contains
frames with similar intensity. After that, the improved NPELO (Near-Perfect Estimation
for Local Optimality) [71] and MVRBR (Motion Vector Reversion- Based steganalysis
Revisited) [72] features were extracted from each class and fed to an independent SVM
classifier. The independent classifiers were given different weights depending on the
intensity amount of the frames, where the classifier of the high-intensity class has a higher
weight. Finally, the integrated classifiers detect the video whether is cover or stego. The
used database contains 100 YUV sequences, each sequence has 150 to 300 frames with
Symmetry 2022, 14, 117 19 of 26

30 fps in CIF format. The database was addressed in the H.264/AVC standard by the
×264 tool.
Another steganalytical technique based on MV is proposed by Sadat et al. [61], where
the entropy and motion estimation field is utilized for selecting the features. After dividing
the frame into blocks, local optimization of the cost function is used to extract intrinsic and
statistical features include the sum of absolute differences (SAD) and the sum of absolute
transformed differences (SATD). Then all blocks have given weight depending on the
amount of texture, where high textures gave a high weighted value in decision making
during training of the SVM classifier. For evaluation, 284 video sequences have been used
which were downloaded from the Internet. Their technique obtained high accuracy up
to 99.9%.
Spatial and temporal motion features are considered simultaneously in the tech-
nique of Tasdemir et al. [62]. The frames are first divided into three-dimensional blocks
(8 × 8 × temporal axis). Then, from each block, three histograms are computed for the
three dimensions, then the motion and texture features are extracted. After calculating
these features, the blocks are categorized into three classes, where the first-class contains
the blocks in which its features remained unchanged; the second-class contains the blocks
with slight changes, and the third-class includes the blocks containing a large change. Each
class is given a weight value that is identified empirically. For the classification task, the
comprehensive presentation of the spatiotemporal features and the weighted modulation
are fed to the SVM for training. The used database contains 14 YUV sequences; only the
first 90 frames of each sequence are used for the experiments. The database was addressed
in the MPEG2 and H.264 formats standard. The authors have proved that using spatial
and temporal simultaneously can increase detection accuracy by 20 % and 5% in low and
high payloads, respectively, compared with seven different steganalysis techniques.
Recently, Li et al. [63] proposed a steganalytical technique for HEVC video steganogra-
phy. The frame in the HEVC standard can divide into the same size code tree unit (CTU). In
the addition, CTU can divide into smaller code units (CU), each CU can further divide into
a transform unit (TU) and prediction unit (PU) as illustrated in Figure 11. Their technique
is based on the fact the PU partition modes would be changed after embedding the data.
Hence, they selected the rate of change of PU partition modes in the cover- and stego-video
as features. These features are the input for the SVM classifier. According to the experiment,
the detection accuracy reaches approximately 93%.

Figure 11. The partition CTU and PU in HEVC standard.

Ghamsarian et al. [64] proposed a blind technique to detect many types of MV

steganography. A novel feature called MVs’ Spatio-Temporal features termed (MVST)
is proposed where consists of 36 and 18 spatial and temporal feature sets, respectively.
The features are extracted from the various partitions of H.264/AVC standard instead of a
fixed size of blocks. For computing the detection accuracy of the proposed technique, the
SVM classifier is utilized in four situations. One of them is a real-world situation where
Symmetry 2022, 14, 117 20 of 26

the technique does not have any information regarding the steganography techniques and
the embedding rate. The experiment result on the 22 PAL QCIf video dataset showed the
stability and high detection accuracy reach 95% in a real-world situation.
The first universal steganalysis technique based on deep learning was proposed
by Liu and Li [65]. The proposed noise residual feature has arisen from the fact of the
intra-prediction mode and motion vector steganography techniques affect the pixel values
of the decoded frames. Therefore, the authors developed an NR-CNN framework to
extract features from noise residuals and learn the steganographic noise residual features
that are independent of the content of the frame. The fully connected layer and softmax
classifier are used for binary classification. The experimental dataset was contained 200,000
frames for training, 20,000 for verification, and 200,000 frames for testing. The experiments
demonstrated satisfying results regarding low embedding rate, and high performance in
the case of a high embedding rate with 59.82% and 99.74% detection accuracy for intra
prediction, respectively, and 62.53% and 95.39% detection accuracy for MV, respectively.
Huang et al. [66] proposed the first deep learning-based video quantitative steganaly-
sis technique. The features are extracted from 4 × 4 PU of each frame since it is the most
basic unit in the HEVC video standard. To ensure the robustness of the neural network
against low and high bitrates that exist in the same video, each of the motion vectors and
the prediction matrices has been calculated, respectively, for each 4 × 4 PU. These matrices
are fed to CNN, which is consequently, extracts a 512-dimensional feature vector and
submits it to the last fully connected layer. The softmax classifier is used to detect the stego-
video and estimate the bitrate. The experimental results on the Xiph Video Test Media
database demonstrated that the proposed techniques can estimate different embedding
rates with low mean absolute error MAE.

5. Databases
A standard database is necessary to evaluate and compare the results of the existing
techniques. In this section, we review the existing datasets used for evaluation. Table 6
provides a minor description for the existing global trusted datasets. It is clear that there are
few public datasets for audio steganalysis, while most are for image steganalysis. However,
the ASD and ASDIIE datasets are provided recently which will inspire many researchers in
audio steganalysis. On the other hand, currently there is no available standard dataset for
video steganalysis. As in the audio steganalysis field, the researchers in video steganalysis
use their own datasets to evaluate, which leads to unfair comparisons.

Table 6. The available datasets in the Steganalysis Field.

Name Media Type Description Availability

No. of images: 10,000, Format: Portable Gray
BOSSBase V1.01 [73] Image Public
Map (PGM) of 8 bits, Size: 512 × 512
No. of images: 10,000, Format: PGM of 8 bits,
BOWS2 [74] Image Public
Size: 512 × 512
No. of images:more than 14 million, Format:
ImageNet [75] Image Public
mostly JPEG, Size: different sizes
No. of images: 960,000, Format:DNG and
Stegoappdb [76] image Public
JPEG, Size: different sizes
No. of images: 10,800, Format: DNG and JPEG,
Corel [77] Image Public
Size: different sizes
No. of images: 237 and 96 in 4 sequences,
USC-SIPI [78] Image and video Public
Format: TIFF, Size: different sizes
No. of clips: 22,671, Format:WAV with a
Audio Steganalysis Dataset IIE (ASDIIE) 1 Audio Public
sampling rate of 44.1 kHz and duration of 10 s
No. of clips: 33,038, Format: MP3-WAV,
Audio Steganalysis Dataset [79] Audio Public
Sampling rate: 44.1 kHz, Duration: 10 s
No. of clips: 320 Chinese and English speech,
Speech dataset [80] Audio Public
Format: PCM
1 Available in: https://ieee-dataport.org/documents/audio-steganalysis-dataset#files, accessed on

11 October 2021.
Symmetry 2022, 14, 117 21 of 26

6. Evaluation metrics
The steganalysis approach always produces binary classification classes (cover and
stego), so the accuracy, detection rate, and error rate metrics are enough to evaluate the
steganalysis techniques. Given the following:
• tp: stego-media classified as stego-media
• tn: cover-media classified as cover-media
• fp: stego-media classified as cover-media
• fn: cover-media classified as stego-media
Then:
Accuracy = (tp + tn)/all
DetectionRate (TRP) = tp/(tp + fn)
ErrorRate (FPR) = fp/(fp + tp)
In addition, the ROC curve is mostly used to evaluate and compare the classification
task of many steganalysis techniques. It presents the True Positive Rate with respect to the
False Positive Rate, where FPR = 0 and TPR = 1 indicates a perfect detector.

7. Digital Multimedia Steganalysis Tools

There are various software tools that exist which allow easy detection the hidden
data in digital multimedia [45]. Most of them are targeting well-known steganography
techniques. Although most of the researchers focused on steganalysis methods, few papers
are focused to evaluate the existing steganalysis tools [45,81]. However, a comparison
between some of the popular steganalysis tools is provided in paper [45].
Table 7 summarizes the most popular existing steganalysis tools for digital media:
audio, image, and video. As illustrated in the table, most of the steganalysis tools are
designed for detecting the hidden data in images, while a few tools deal with video and
audio mediums.

Table 7. The Existing Steganalysis Tools.

Software Producer Platform Medium Type Availability

StegSpy [82] SpyHunter Windows Image Freeware
StegDetect [83] Niels Provos Linux, windwos Image Freeware
StegSecret [84] Alfonso Muñoz Java-based Image, Audio, Video Freeware
StegoHunt [85] WetStone Technologies Windows Image, Audio, Video License purchase
StegExpose [86] Benedikt Boehm Java-based image Freeware
StegAlyzerAS [87] Backbone Security Windows Image License purchase
StegalyzerSS [88] Backbone Security Windows Image License purchase
StegAlyzerFS [89] Backbone Security Windows, Linux, Apple OS Image, Audio, Video License purchase
VSL [90] Michal Wegrzyn Java-based Image Freeware

8. Perspectives and Open Issues

In this section, we will discuss the existing status in this domain and present some
open issues that need to take into consideration in future works.
• Standard dataset: For an easier and fair comparison between the published ste-
ganalysis techniques, it is necessary to use a standard and fixed dataset. In image
steganalysis, there are standard databases where the most popular are BOSSbase and
BOWS2 databases. On the other hand, audio steganalysis was until recent lacked for
the standard dataset, where the researchers generated their own datasets. Fortunately,
a public dataset for audio steganalysis was generated for WAV, mp3 formats. However,
still there is a need for developing a unified and public dataset for video and other
formats for audio steganalysis.
• Practical steganalysis techniques: Computation complexity and time consumption
are two significant terms that should be considered when developing practical ste-
Symmetry 2022, 14, 117 22 of 26

ganalysis techniques. Most of the existing techniques give attention to accuracy

detection regardless of the complexity of the computation.
Nowadays, the emerging of machines and deep learning opens a new horizon for
faster and more accurate detection. Although of their advantages, many issues should
be taken into consecration such as avoiding the complexity of the training phase,
adjusting and fixing well hyperparameters before training, and avoid overfitting.
• Virtual and online services: The digital technology era increased the chances for
criminals to exploit new unsuspicious spots for hiding such as online games, cloud
storage systems, and the virtual world. Steganalysis techniques for these network
traffic services should be developed.
• Balancing: There is a clear unbalancing in this domain, starting with the proposed
steganography and steganalysis techniques. As illustrating in Figure 12, there is a
big gap between the number of published steganography and steganlysis techniques.
This gap back to the difficulty and obstecals that faced by researcher and forensic
expertsin the steganalysis field. So, there is highly needed for more investigation and
work from dedicated and adept researchers in steganalysis domain.
Regarding medium based steganalysis, image steganalysis, then audio, receives more
attention from the researcher than video steganalysis. However, researchers have
shown that by 2021, video traffic will consume 82% of all traffic of the Internet [61].
Besides that, the video medium provides sufficient space for hiding data which makes
the video medium a good scope to exploited by steganography technique. So, video
steganalysis should attract more attention from the researchers.
In addition, the researchers focus on some formats more than others. For example,
in audio steganalysis, most of the existing techniques are proposed for mp3 format,
especially for Mp3stego steganography. Among these techniques, LSB techniques
have the biggest share, where the non-LSB techniques need to more investigate [16].
• General steganalysis technique: Most of the existing steganlysis techniques relied
on the training dataset to make the classifier learns and trains on the representation of
cover and stego medium (semi-blind) or only cover medium (blind). Hence, there a
high relation between classification accuracy and training datasets, where mostly the
accuracy reduces as the testing dataset differs from the training dataset. Indeed, most
of the existing techniques are belong to the semi-blind approach.
In the real world, there are thousands of steganography techniques and different types
of cover contents, size, noise, and sampling frequency, etc. This amount of training
datasets especially for stego- mediums makes it impossible to restrict them. There are
a few techniques that exploit the unsupervised approach to deal with this issue [91],
but still, the accuracy needs to be improved.
• Keeping up: The steganalyser should be keeping up with the new trends related
to steganography and steganalysis domain. For instance, the recent formats as
H.265/HEVC will be highly exploited for steganography, since it contains various
advanced features.
IoT (Internet of Things) has been utilized by a few researchers for the transfer and
storage of data hidden using steganography. So, there is a big chance to used by
criminals for hiding the secret data [25].
Last but not least, the steganalysis techniques deal with digital medium audio, image,
and video separately. However, the video file normally contains video coding format
alongside audio data in an audio coding format. The sound-video or audio-video
provides a high capacity for embedding secret data for the criminal. Although there
are few steganography techniques in this regard [92–94], no steganalysis technique
available till date. However, the existing image and audio steganalysis could detect
that type of steganography, but there are no experiments for evaluation.
Symmetry 2022, 14, 117 23 of 26

Figure 12. Frequency of published steganography and steganalysis research articles throughout the
years. Where St : steganography and Sa: steganalysis. Taken from [25].

9. Conclusions
This survey provided an overview of the basic concepts of steganography and ste-
ganalysis and their classification. In addition, a comprehensive review of the recent research
on steganalysis techniques for audio, image, and video mediums was provided in detail. The
applications, datasets, and popular tools available for steganalysis were mentioned. In
the end, this survey discussed the main shortcomings in this domain and suggested some
future recommendations.

Author Contributions: Conceptualization, M.J.A.; methodology, D.A.S. writing—original draft

preparation, D.A.S.; writing—review and editing, D.A.S. and M.J.A.; visualization, D.A.S.; supervi-
sion, M.J.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: This project was funded by the Deanship of Scientific Research (DSR) at King
Abdulaziz University, Jeddah. The authors, therefore, acknowledge with thanks DSR for technical
and financial support.
Conflicts of Interest: The authors declare no conflict of interest.

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