# Reversible Adversarial Attack based on Reversible Image Transformation

arXiv: https://arxiv.org/abs/1911.02360 · HTML: https://mlsanity.com/paper/1911.02360/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Reversible Attack based on Local Visual Adversarial Perturbation](https://mlsanity.com/paper/2110.02700/) (2021)
- [ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models](https://mlsanity.com/paper/2412.17038/) (2024)
- [Invertible Image Dataset Protection](https://mlsanity.com/paper/2112.14420/) (2021)
- [RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery](https://mlsanity.com/paper/2311.12858/) (2023)
- [DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection](https://mlsanity.com/paper/2505.06860/) (2025)

## Abstract (arXiv)

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.