Decoupling-GAN for camera model identification of JPEG compressed images

In recent years, many forensic methods are proposed for camera model identification (CMI). These methods expose the acquisition devices of the images according to the traces left during the imaging process. However, such traces could be easily affected by common image operations, e.g., JPEG compression, which make the camera model identification of post-processed images very difficult. In this paper, we propose a GAN based decoupling network (Decoupling-GAN) to boost the performance of CNN-based model detectors for JPEG compressed images by alleviating the effects of JPEG compression on the camera model identification. Through the adversarial training, we rebuilt the consistency of extracted feature maps between the original images and JPEG compressed ones. Experimental results show that Decoupling-GAN exhibits good robustness performance and outperforms prior arts in terms of detection accuracy under JPEG attacks.