Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization
Xiuli Bi, Zhipeng Zhang, Bin Jie Xiao · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021
When many forgery images become more and more realistic with help of image editing tools and convolutional neural networks (CNNs), authenticators need to improve their ability to verify these forgery images. The process of generating and detecting forgery images is the same as the principle of Generative Adversarial Networks (GANs). In this paper, since the retouching progress of forgery images requires to suppress the tampering artifacts and to keep the structural information, we consider this retouching progress as an image style transform, and then propose a fake-to-realistic transform generator GT. For detecting the tampered regions, a localization generator GMis proposed too, which is based on a multi-decoder-single-task strategy. By adversarial training two generators, the proposed α-learnable whitening and coloring transform α-learnable WCT) block in GTautomatically suppress the tampering artifacts in the forgery images. Meanwhile, the detection and localization abilities of GMwill be improved by learning the forgery images retouched by GT. The experiment results demonstrate that the proposed two generators in GAN can simulate confrontation between the faker and the authenticator well; the localization generator GMoutperforms the state-of-the-art methods in splicing forgery detection and localization on four public datasets.