An open-source digital image forensic toolset

Image Forgery Detection and Localization (and related) Papers List

Official Code for ICCV 2021 paper "Towards Flexible Blind JPEG Artifacts Removal (FBCNN)"

Learn how to research images and the tools, techniques & tradecraft required.

[ICCV 2023] Official implementation of the paper: "DIRE for Diffusion-Generated Image Detection"

Official code for CAT-Net: Compression Artifact Tracing Network. Image manipulation detection and localization.

[CVPR 2022 Oral] Detecting Deepfakes with Self-Blended Images https://arxiv.org/abs/2204.08376

[CVPR'19, ICLR'20] A Python toolbox for modeling and optimization of photo acquisition & distribution pipelines (camera ISP, compression, forensics, manipulation detection)

Copy-move image forgery detection library.

[CVPR 2023 Highlight] Official implementation of the paper: "AltFreezing for More General Video Face Forgery Detection"

❤️ Free batch image geolocation and digital forensics tool. Automatically extract .jpg EXIF data, visualize GPS coordinates on maps, and reconstruct event timelines for OSINT.

A collection of deep learning approaches and datasets publicly available for image forgery and deepfakes detection

phoenix is a small command line image forensics tool

GAN-generated image detection based on CNNs

Copy-Move forgery database with similar but Genuine objects. ICIP2016 paper

Tampering detection related sources

Detection of copy-move forgery in an image with CMDF methods. (SIFT, SURF, AKAZE, RANSAC)

IFAKE is an application for detecting image and video forgery, designed to help users verify the authenticity of digital media. This repository also contains the AI model and dataset that we developed for image tampering detection, providing an effective solution for detecting image and video manipulations.

Corrections of resolution issue for common image manipulation localization datasets. (CASIA, Coverage, IMD2020)

Author implementation of Exploring Adversarial Fake Images on Face Manifold (CVPR 2021 oral)

Computer Graphics vs Real Photographic Images : A Deep-learning approach

VAAS is an inference-first, research-driven library for image integrity analysis. It integrates Vision Transformer Attention Mechanisms with patch-level self-consistency analysis to enable fine-grained localization and detection of visual inconsistencies across diverse image analysis tasks.

aim of this project is to give insight into authenticity of an image using ELA and metadata analysis based weather validation

Reproduced Code for Image Forgery Detection papers.

Implementation of the paper A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer

Fusion Transformer with Object Mask Guidance for Image Forgery Analysis

Extract camera fingerprint using different types of state-of-the-art denoisers

This is the one of solution implemented for image forgery localization using deep learning techniques and architectures such as UNET, VGG

[ECCV 2022: Oral] In this work, we discover that color is a crtical transferable forensic feature (T-FF) in universal detectors for detecting CNN-generated images.

Code for paper "Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation"