标题： 基于可逆图像变换的可逆对抗样本 显示英文标题

标题： Reversible Adversarial Example based on Reversible Image Transformation

摘要： 目前，深度神经网络（DNNs）在许多领域取得了重大突破，并被广泛用于自动分析和分类多媒体数据。 然而，DNNs 也可以用于某些恶意检测，特别是针对来自各种社交平台的图片，这对用户隐私构成了严重威胁。 对抗样本的攻击特性可以欺骗这种人工智能系统，以实现隐私保护等目的。 可逆的对抗样本实现了对人工智能系统的可逆网络攻击，这不仅禁用了系统的自动分类和分析功能，还能无错误地恢复原始图像。 然而，在现有的可逆对抗样本的研究中，存在诸如难以完全嵌入对抗扰动信息，导致原始图像恢复失败，以及由于扰动约束导致的攻击效果无法满足隐私保护要求等问题。 在本文中，我们利用可逆图像变换（RIT）实现了原始图像与其可逆对抗样本之间的直接转换。 实验结果表明，所提出的方案不仅可以成功地无失真地恢复原始图像，而且不受扰动幅度的限制，能够带来更高的攻击成功率以达到所需的隐私保护目标，同时确保图像失真对人眼不可察觉。 显示英文摘要

摘要： Currently, Deep Neural Networks (DNNs) have made major breakthroughs in many fields, and are widely taken to automatically analyze and classify multimedia data. However, DNNs can also be used for certain malicious detection, especially for pictures from various social platforms, posing a serious threat to user privacy. The attack characteristics of adversarial examples can fool such artificial intelligence systems to achieve privacy protection and other purposes. The reversible adversarial example achieves the reversible cyber attacks on the artificial intelligence system, which not only disable the system's automatic classification and analysis functions, but also restores the original image error-free. Nevertheless, in the existing work of reversible adversarial examples, there are problems such as the difficulty of fully embedding the adversarial-perturbations information, resulting in the failure of the original image restoration, and the inability of the attack effect caused by the perturbation constraint to meet the privacy protection requirements. In this paper, we take advantage of Reversible Image Transformation (RIT) to realize direct conversion between the original image and its reversible adversarial example. Experimental results show that proposed scheme can not only successfully restore the original image without distortion, but also is not limited by the perturbation amplitude, can bring a higher attack success rate to reach desired privacy protection goal, while ensuring that the image distortion is imperceptible to the human eyes.