Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters that are available in several image processing libraries, modern cameras, and mobile applications. The multi-objective optimization takes into account not only the attack success rate but also the detection rate. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks.
翻译:最近的研究显示,深精模型容易出现对抗性实例,即一般图像中的数据,被故意修改,以欺骗机器学习分类师。在本文中,我们提出了一个多目标嵌套进化算法,以便在黑箱情景中产生普遍、无限制的对抗性实例。无限制袭击是通过应用几个图像处理图书馆、现代相机和移动应用程序中现有的众所周知的图像过滤器进行的。多目标优化不仅考虑到攻击成功率,还考虑到探测率。实验结果显示,这种方法能够产生一系列能够产生非常有效和无法探测的攻击的过滤器。