Image Forgery Detection and Localization (and related) Papers List

AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics, WMF@CVPR2023

This GitHub provides different DeepFakes Detectors using facial regions and considering three different state-of-the-art fake detection systems.

Agent skill for deepfake detection & media safety — detect AI-generated audio, images, and video with Resemble AI

This repository contains the official implementation (PyTorch) of "Multimodal Forgery Detection Using Ensemble Learning" proposed in APSIPA Paper 2022.

Three forensic worlds. One conversation. Zero paperwork

Deep learning system achieving 95.36% accuracy in media forgery detection using hybrid ResNet50+ViT architecture. Optimized for 20% training data efficiency with PyTorch, OpenCV, and Flask-based inference pipeline. Live demo on Hugging Face Spaces.

🎭🔍 Detecting deepfake videos and images using Deep Learning with a Tkinter desktop interface

Production-ready Multimodal Lip Sync Detection & Deepfake Detection System. Detects audio-video synchronization mismatches using deep learning (PyTorch) with a scalable FastAPI-based inference pipeline. Optimized for real-time processing,low false positives, and robust performance on noisy speech segments. Built for video forensics,synthetic media

AI-powered deepfake detection system using Deep Learning and Computer Vision to identify manipulated facial images and videos with high accuracy.

Multimodal Deepfake Detection Framework | Visual (EfficientNet+FFT) + Audio (MFCC) + Temporal (Transformer) | Attention Fusion | WildDeepfake | PyTorch

AI-powered multimodal deepfake detection system leveraging audio-visual fusion, transformer architectures, physiological analysis, and forensic intelligence for robust media authenticity verification.

InSwapper Detector is a production-focused deepfake detection system designed to identify faces manipulated with INSwapper. It combines face detection, RGB image analysis, frequency artifact extraction, and a multi-task ConvNeXt-Tiny model for image, batch, and video-based detection.

Detect AI-generated face manipulations in real time — with visual explanations showing exactly where the model found tampering. Supports images, videos, and live webcam. Powered by EfficientNet-B4 and Grad-CAM XAI.

An Agentic AI system for Deepfake Detection & Media Authenticity Verification. Features autonomous model routing, an ensemble of 6 specialized neural networks, and advanced bias correction mechanisms.

A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

Beta tool for embedding and verifying digital authenticity in images, audio, and video using watermarks, C2PA, and steganography.

Explainable deepfake & AI-content authenticity platform — FastAPI + React. Pluggable image/audio/video detectors that return a probability + plain-language reasons, with API keys, async jobs & signed webhooks.

TruthBeam — projector–camera provenance: verifiable, tamper-evident recording (truthbeam.com)

A modular PyTorch-based research lab for deepfake detection. Includes implementations and experiments with autoencoders, classifiers, contrastive learning, and generative-model–based detection techniques. Designed for benchmarking, reproducibility, and rapid exploration of new architectures.