Attacking Forgery Detection Models Using a Stack of Multiple Strategies

In the digital age, identifying forgery in images has become a significant challenge for image analysis systems and applications. In this study, we approach this issue from a different perspective compared to existing methods for this problem, proposing an attack method to forgery detection models. Instead of the traditional approach of randomly modifying images, we combine a range of diverse strategies, ranging from the utilization of diffusion models to the implementation of camera trace erasing techniques aimed at eliminating the remnants left by forgers, consequently leading to the deception of forensic methods. This is not a simple problem, it requires a strategy to choose which models and in what order is most appropriate to both attack effectively and ensure that image quality is not reduced. Experimental results demonstrate that our method not only surpasses modern counter-forensics methods but also preserves the naturalness and semantics of the processed images. In this way, our research provides a valuable contribution to understanding and responding to the threats faced by modern image analysis systems.