Image tampering localization model for intensive post-processing scenarios - TAN Shunquan; LIAO Guiying; PENG Rongxuan; HUANG Jiwu (Tongxin xuebao)
Addressing the challenges of blurred or destroyed tampering traces presented by lossy operations such as image compression and scaling on images within social platforms like WeChat and Weibo, an adversarial image tampering localization model was introduced. Utilizing the pyramid vision transformer, which was built upon the Transformer architecture, as an encoder for extracting tampering features from images. Simultaneously, an end-to-end encoder-decoder structure, reminiscent of the UNet architecture, was formulated. The pyramid structure and attention mechanisms inherented to the pyramid vision transformer afforded a flexible examination of diverse image regions. When integrated with the UNet-like architecture, it facilitated multiscale contextual information extraction, thereby fortifying the model's resilience to intense post-processing effects. Empirical results illustrate that the proposed model exhibits a substantial performance advantage over conventional tampering localization models, particularly in scenarios involving prevalent post-processing techniques such as JPEG compression and Gaussian blur. Notably, the model demonstrates exceptional robustness in assessments conducted with datasets representing diverse social media dissemination scenarios.