Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

TL;DR

This paper introduces a deep multi-scale discriminative network (MSD-Net) for blind JPEG tampering detection, automatically extracting features from DCT histograms and effectively identifying tampered regions, especially under challenging compression scenarios.

Contribution

The paper proposes a novel MSD-Net architecture that automatically extracts multi-scale features and enhances detection in difficult compression cases, advancing JPEG tampering forensics.

Findings

Outperforms state-of-the-art methods in accuracy

Effectively detects tampering even with high-quality compression

Provides precise localization of tampered regions

Abstract

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this paper, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.