Reversible Adversarial Attack based on Reversible Image Transformation
This work addresses the need for secure, reversible image protection against unauthorized access, but it appears incremental as it builds on existing reversible adversarial attack techniques.
The paper tackled the problem of generating reversible adversarial examples (RAE) with minimal distortion and error-free recovery, achieving imperceptible image distortion and maintaining attack ability regardless of perturbation amplitude.
In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise. Reversible adversarial examples (RAE) get both attack capability and reversibility at the same time. However, the existing technique can not meet application requirements because of serious distortion and failure of image recovery when adversarial perturbations get strong. In this paper, we take advantage of Reversible Image Transformation technique to generate RAE and achieve reversible adversarial attack. Experimental results show that proposed RAE generation scheme can ensure imperceptible image distortion and the original image can be reconstructed error-free. What's more, both the attack ability and the image quality are not limited by the perturbation amplitude.