TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection
This work addresses a domain-specific problem in multimedia forensics by improving detection accuracy for color image manipulation chains, though it appears incremental as it builds on existing detection methods.
The paper tackles the generalization problem in color image operation chain detection by proposing a two-stream multi-channels fusion network that leverages spatial artifacts and noise residuals, achieving state-of-the-art generalization ability and robustness to JPEG compression.
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.