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Mini Project

The document outlines a project focused on developing an AI-based multi-modal deepfake detection system that analyzes videos, audio clips, and images to verify their authenticity. Utilizing advanced deep learning techniques and trained on publicly available datasets, the system detects signs of media tampering and provides confidence scores along with explainable results. Future enhancements aim to enable real-time integration and monitoring through browser plugins and social media APIs.

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kengarswa
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6 views2 pages

Mini Project

The document outlines a project focused on developing an AI-based multi-modal deepfake detection system that analyzes videos, audio clips, and images to verify their authenticity. Utilizing advanced deep learning techniques and trained on publicly available datasets, the system detects signs of media tampering and provides confidence scores along with explainable results. Future enhancements aim to enable real-time integration and monitoring through browser plugins and social media APIs.

Uploaded by

kengarswa
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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“AI-Based Multi-Modal

Deepfake Detection for Video,


Audio, and Images”
📄 Abstract:
In the era of generative AI, the manipulation of media content has
become alarmingly realistic and accessible. Deepfakes — synthetic
videos, images, and audio generated using deep learning techniques —
pose significant threats in the form of misinformation, identity theft,
cyberbullying, and fraud. This project presents a comprehensive
solution: an AI-based multi-modal deepfake detection system capable of
analyzing and verifying the authenticity of videos, audio clips, and
images.
Utilizing advanced deep learning techniques, including Convolutional
Neural Networks (CNNs), recurrent models, and pretrained
architectures like XceptionNet, MesoNet, and Wav2Vec, the system
detects signs of media tamperin+g, such as facial inconsistencies,
unnatural blinking, synthetic voice patterns, and image noise artifacts.
The system is trained on publicly available datasets such as
FaceForensics++, Celeb-DF, and VoxCeleb, ensuring ethical data
handling.
The proposed solution not only performs content-based analysis but
also provides a confidence score and explainable results to highlight
regions or features deemed suspicious. Future enhancements include
real-time integration, browser plugins, and social media APIs for live
monitoring and media verification.

Technologies Used:

Category Technologies / Tools

Languages Python, JavaScript

Frameworks PyTorch, TensorFlow, Flask/Django

Deep
XceptionNet, MesoNet, Wav2Vec, EfficientNet
Learning

Librosa, SV2TTS, Tacotron 2, Mel-


Audio Tools
Spectrograms

OpenCV, FFmpeg, Dlib, First Order Motion


Video Tools
Model (FOMM)
Category Technologies / Tools

Image Noise residuals, pixel-level artifacts, lighting


Forensics checks

Visualization Matplotlib, Seaborn

Deployment Flask API, optional React.js front-end, Docker

FaceForensics++, VoxCeleb, Celeb-DF,


Datasets
FakeAVCeleb

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