TAE-RWP: Traceable Adversarial Examples With Recoverable Warping Perturbation

Abstract

Reversible adversarial example (RAE) is an effective cutting-edge technology for protecting the intellectual property (IP) of datasets. However, existing RAE schemes primarily focus on the adversarial and restoration capabilities of adversarial examples (AE), with little attention paid to traceability, which is crucial for IP protection. This oversight leads to the inability to prevent authorized users from redistributing data, thereby posing significant IP security risks. To address this issue, we propose a novel approach named TAE-RWP, wherein adversarial perturbations in AEs are treated as tools for IP verification. To enable the traceability of AEs, we introduce varying degrees of warping to the adversarial perturbations within the AEs of authorized users, utilizing the warping degree as a traceable feature. To further strengthen traceability, we adopt a technique named “random warping” to maintain the resilience of adversarial perturbations against distortions, and employ a strategy named “noise mode” to improve the verification model’s capacity to recognize distortion features. Experimental results indicate that AEs generated by TAE-RWP exhibit remarkable adversarial strength and restoration abilities, while the verification model demonstrates excellence in recognizing distortion features.