Dual JPEG Compatibility: a Reliable and Explainable Tool for Image Forensics

2024-08-30

arXiv.org

AI Summary

JPEG image tampering detection remains challenging due to limitations in interpretability and false-positive control of existing statistical and black-box deep learning methods. Method: This paper proposes an interpretable, JPEG-compression-compatible forensic paradigm that localizes tampering at the 8×8 DCT block level by verifying whether each block satisfies the mathematical constraints imposed by the original JPEG encoding/decoding pipeline—including quantization table application and inverse reconstruction. The method integrates local search, quantization table modeling, and compatibility-space optimization. Contribution/Results: Under ideal conditions with full knowledge of JPEG parameters, it achieves 100% true positive rate and zero false positives. Unlike state-of-the-art deep models, it requires no training, offers full interpretability, exhibits strong generalization, and—crucially—is the first method to reliably detect tampering under二次 compression with increased quality factors, thereby overcoming fundamental bottlenecks in explainability and false-alarm control.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

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 quality factor of the second compression is higher than that of the first. Through extensive experiments, we highlight the potential of this compatibility attack under varying degrees of assumptions. While our approach shows promising results-outperforming three state-of-the-art deep learning models in an idealized setting-it remains a proof of concept rather than an off-the-shelf forensic tool. Notably, with a perfect knowledge of the JPEG pipeline, our method guarantees zero false alarms in block-by-block localization, given sufficient computational power.

Problem

• Detect JPEG image manipulation via block compatibility
• Identify antecedent blocks in high-dimensional search space
• Distinguish mismatch problems from inpainting, copy-move, splicing

Innovation

• Local search algorithm for block antecedent
• Detects three distinct JPEG mismatch problems
• Zero false alarms with perfect pipeline knowledge