Detecting Deepfakes Without Seeing Any
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Updated
Jul 25, 2024 - Python
Detecting Deepfakes Without Seeing Any
Forensight is a powerful Image OSINT + Real-time video call inspection tool for digital investigations. It automates image, metadata, and network intelligence gathering with precision tools like facial recognition, EXIF recovery, object detection, OCR, and footprint tracing — giving analysts hacker-grade insight into digital evidence.
DeepFake detection using GAN and DeepLearning
🚀 Deepfake Detector – Open-source tool to spot fake images, videos & audio using EfficientNetV2 + MTCNN. ⚡ Frame-by-frame analysis for high-accuracy detection, built simple for researchers, devs & security analysts.
Comprehensive Protection Against Deepfakes, Deepnudes & Harmful Content
Deep-PoC is a deepFake detection tool designed to detect deepfakes from videos or images using artificial intelligence.
Application that detects the authenticity of audio files developed using the Random Forest Model.
UnFake is the first platform to integrate a deepfake detection tool directly into the image-downloading process. Check Live by pressing below link -->
Project Work in Generative Models UNIFI, Deep Fake Detection
A deep learning based research to encourage healthy online information sharing by detecting and removing deep-fakes to avoid the spread of misleading information on the internet.
DeepFake Audio detection project using Wav2Vec2 for MOMENTA (Task for internship )
Deep-fake medical image(X-ray) using GAN
Deepfake Detection using SWIN Transformer
A semester project using ShuffleNet, MobileNetV3 Small & ResNet50 to classify real and fake faces with the specified dataset that taken from Kaggle.
Research on possible strategies to perform artificial music detection through classical machine learning models
A cinematic AI command center leveraging Gemini 2.5 Flash to detect multimodal scams through real-time audio, image, and text threat intelligence.
A novel training framework for image-generating GANs which penalizes synthesis artifacts by computing a Fourier dissimilarity between synthesized and real images. Thus, it improves the detection evasiveness of GANs.
On this repo there is a University project about the course of Signal, Image and Video where a constraint was to bulid a project where AI is just a narrow task and not the whole implementation of the project. I will just use AI as a comparison with traditional techniques of video processing.
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