TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection

TL;DR

TMFNet introduces a dual-stream deep learning approach that effectively detects color image operation chains by leveraging spatial artifacts and noise residuals, achieving superior generalization and robustness to compression artifacts.

Contribution

The paper presents a novel two-stream multi-channel fusion network with a pooling-free residual architecture and a correlation-aware filter design for improved operation chain detection.

Findings

Achieves state-of-the-art generalization performance.

Maintains robustness under JPEG compression.

Effectively captures multi-channel correlation features.

Abstract

Image operation chain detection techniques have gained increasing attention recently in the field of multimedia forensics. However, existing detection methods suffer from the generalization problem. Moreover, the channel correlation of color images that provides additional forensic evidence is often ignored. To solve these issues, in this article, we propose a novel two-stream multi-channels fusion networks for color image operation chain detection in which the spatial artifact stream and the noise residual stream are explored in a complementary manner. Specifically, we first propose a novel deep residual architecture without pooling in the spatial artifact stream for learning the global features representation of multi-channel correlation. Then, a set of filters is designed to aggregate the correlation information of multi-channels while capturing the low-level features in the noise residual stream. Subsequently, the high-level features are extracted by the deep residual model. Finally, features from the two streams are fed into a fusion module, to effectively learn richer discriminative representations of the operation chain. Extensive experiments show that the proposed method achieves state-of-the-art generalization ability while maintaining robustness to JPEG compression. The source code used in these experiments will be released at https://github.com/LeiTan-98/TMFNet.