PRest-Net: Multi-domain Probability Estimation Network for Robust Image Forgery Detection
Jiaxin Chen, Xin Liao, Zhenxing Qian, Zheng Qin
Abstract
As an important carrier of information transmission in online social networks (OSNs), the authenticity protection of images is of great significance. However, the abuse of image processing technology makes its security questionable. Meantime, lossy operations adopted by OSNs will change the forgery artifacts, which brings challenges to robust image forgery detection. To address this issue, considering the suppression of lossy noise caused by transmission, a novel multi-domain probability estimation network (PRest-Net) is proposed. Firstly, we design a multi-domain probability estimation method to capture the most differentiated regional information from the spatial, residual, and wavelet domains. Since the wavelet coefficient of semantic information is larger than that of lossy noise, and the edge texture can be highlighted in the residual image, the negative effect of lossy noise would be reduced and semantic forgery traces can be exposed more easily. We further design a forgery detector composed of low-level feature extraction, high-level feature extraction, and regional edge difference learning module, which can adaptively learn rich forgery clues. Extensive experimental results are provided to validate the superiority of PRest-Net compared with existing state-of-the-art detectors in the scenarios of detecting forged images transmitted over various OSNs.