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.
The aim of operation chain detection for a given manipulated image is to reveal the operations involved and the order in which they were applied, which is significant for image processing and multimedia forensics. Currently, all existing approaches simply treat image operation chain detection as a classification problem and consider only chains of at most two operations. Considering the complex interplay between operations and the exponentially increasing solution space, detecting longer operation chains is extremely challenging. To address this issue, in this work, we devise a new methodology for image operation chain detection. Different from existing approaches based on classification modeling, we strategically conduct operation chain detection within a machine translation framework. Specifically, the chain in our work is modeled as a sentence in a target language, with each possible operation represented by a word in that language. When executing chain detection, we propose first transforming the input image into a sentence in a latent source language from the learned deep features. Then, we propose translating the latent language into the target language within a machine translation framework and finally decoding all operations, arranged in order. Besides, a chain inversion strategy and a bi-directional modeling mechanism are developed to improve the detection performance. We further design a weighted cross-entropy loss to alleviate the problems presented by imbalance among chain lengths and chain categories. Our method can detect operation chains containing up to seven operations and obtains very promising results in various scenarios for the detection of both short and long chains.