Jielun Huang

RevINN: An End-to-End Invertible Neural Network for Reversible Adversarial Examples Generation

Recent studies have shown that Reversible Adversarial Examples (RAE) can mislead unauthorized deep neural networks while remaining usable for authorized users, effectively preventing image data leakage. Existing RAE methods rely on reversibly embedding perturbation information into the original adve