Comprint: Image Forgery Detection and Localization using Compression Fingerprints

TL;DR

Comprint is a new image forgery detection method that uses compression fingerprints to identify manipulated images, demonstrating high accuracy and improved performance when combined with Noiseprint, especially in real-world scenarios.

Contribution

Introduces Comprint, a novel compression fingerprint-based forgery detection method trained on pristine data, and combines it with Noiseprint for enhanced in-the-wild manipulation detection.

Findings

High accuracy on five diverse datasets

Fusion with Noiseprint significantly outperforms existing methods

Effective in detecting various manipulation types in real-world images

Abstract

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.