# Comprint: Image Forgery Detection and Localization using Compression Fingerprints

arXiv: https://arxiv.org/abs/2210.02227 · HTML: https://mlsanity.com/paper/2210.02227/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization](https://mlsanity.com/paper/2009.00726/) (2020)
- [OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild](https://mlsanity.com/paper/2107.14480/) (2021)
- [Constrained R-CNN: A general image manipulation detection model](https://mlsanity.com/paper/1911.08217/) (2019)
- [Forensic Similarity for Digital Images](https://mlsanity.com/paper/1902.04684/) (2019)
- [Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1708.00930/) (2017)
- [Exposing Fake Images with Forensic Similarity Graphs](https://mlsanity.com/paper/1912.02861/) (2019)

## Built on by (most cited first, 9 of 9)

- [AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics](https://mlsanity.com/paper/2304.06870/) (2023)
- [Weakly-supervised deepfake localization in diffusion-generated images](https://mlsanity.com/paper/2311.04584/) (2023)
- [TGIF: Text-Guided Inpainting Forgery Dataset](https://mlsanity.com/paper/2407.11566/) (2024)
- [DeCLIP: Decoding CLIP representations for deepfake localization](https://mlsanity.com/paper/2409.08849/) (2024)
- [A New Benchmark and Model for Challenging Image Manipulation Detection](https://mlsanity.com/paper/2311.14218/) (2023)
- [Diffusion models meet image counter-forensics](https://mlsanity.com/paper/2311.13629/) (2023)
- [Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes](https://mlsanity.com/paper/2407.15169/) (2024)
- [Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection](https://mlsanity.com/paper/2602.03423/) (2026)
- [Training Data Improvement for Image Forgery Detection using Comprint](https://mlsanity.com/paper/2211.14079/) (2022)

## Abstract (arXiv)

Manipulation tools that realistically edit images are widely available, making it easy for anyone to create and spread misinformation. In an attempt to fight fake news, forgery detection and localization methods were designed. However, existing methods struggle to accurately reveal manipulations found in images on the internet, i.e., in the wild. That is because the type of forgery is typically unknown, in addition to the tampering traces being damaged by recompression. This paper presents Comprint, a novel forgery detection and localization method based on the compression fingerprint or comprint. It is trained on pristine data only, providing generalization to detect different types of manipulation. Additionally, we propose a fusion of Comprint with the state-of-the-art Noiseprint, which utilizes a complementary camera model fingerprint. We carry out an extensive experimental analysis and demonstrate that Comprint has a high level of accuracy on five evaluation datasets that represent a wide range of manipulation types, mimicking in-the-wild circumstances. Most notably, the proposed fusion significantly outperforms state-of-the-art reference methods. As such, Comprint and the fusion Comprint+Noiseprint represent a promising forensics tool to analyze in-the-wild tampered images.