Manipulation Classification for JPEG Images Using Multi-Domain Features

Image forensics comprises the analyses and classifications of manipulations that have been applied to images. The ability to classify various manipulations that have been employed in the process of forgery is essential. Techniques to identify multiple manipulations applied to uncompressed images have been reported thus far, but the forensic approach for JPEG images compressed with various qualities has not been proposed. In this paper, we propose the manipulation classification network (MCNet) to exploit multi-domain features of the spatial, frequency, and compression domains. The proposed MCNet learns several forensic features for each domain through a multi-stream structure and distinguishes manipulations by comprehensively analyzing the fused features. Our work jointly considers visual artifacts caused by image manipulations and compression artifacts due to JPEG compression; therefore, rich forensic features can be explored and learned in the training phase. To enable forgery analysis in the real-world environment, data were generated based on twenty types of manipulation algorithms and various compression parameters. To demonstrate the effectiveness of the proposed MCNet, extensive experiments were conducted using state-of-the-art baselines. Compared to these baselines, our proposed method outperforms in terms of multi-class manipulation classification. In addition, we experimentally proved that the fine-tuned model based on the multi-class manipulation task was effective for different forensic tasks such as DeepFake detection or integrity authentication of JPEG images.