Dual JPEG Compatibility: a Reliable and Explainable Tool for Image Forensics
Etienne Levecque (CRIStAL), Jan Butora (CRIStAL), Patrick Bas, (CRIStAL)

TL;DR
This paper introduces a compatibility-based method for JPEG image forensics that detects manipulations by analyzing whether image blocks are consistent with the original JPEG pipeline, showing promising results in controlled settings.
Contribution
It presents a novel approach to detect JPEG manipulations by finding block antecedents, outperforming some deep learning models in idealized scenarios.
Findings
Detects manipulations like inpainting, copy-move, and splicing after JPEG compression.
Recompression with higher quality factor reveals manipulations.
Guarantees zero false alarms with perfect pipeline knowledge.
Abstract
Given a JPEG pipeline (compression or decompression), this paper demonstrates how to find the antecedent of an 8x8 block. If it exists, the block is considered compatible with the pipeline. For unaltered images, all blocks remain compatible with the original pipeline; however, for manipulated images, this is not necessarily true. This article provides a first demonstration of the potential of compatibility-based approaches for JPEG image forensics. It introduces a method to address the key challenge of finding a block antecedent in a high-dimensional space, relying on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, and splicing, when applied after JPEG compression, result in three distinct mismatch problems that can be detected. In particular, if the image is re-compressed after modification, the manipulation can be detected when the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Anomaly Detection Techniques and Applications
