Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, un-restricted adversarial attack has raised great concern and presented a new threat to the AI safety. In this paper, we propose a wavelet-VAE structure to reconstruct an input image and generate adversarial examples by modifying the latent code. Different from perturbation-based attack, the modifications of the proposed method are not limited but imperceptible to human eyes. Experiments show that our method can generate high quality adversarial examples on ImageNet dataset.

翻译：传统的对抗性实例通常是通过在一个小矩阵规范中给输入图像添加扰动噪音而生成的,实际上,无限制的对抗性攻击引起了极大的关注,对AI安全构成了新的威胁。在本文中,我们提出一个波盘-VAE结构,以重建输入图像,并通过修改潜在代码生成对抗性例子。与以扰动为基础的攻击不同,对拟议方法的修改并不局限于干扰性攻击,而是人类眼睛无法察觉。实验表明,我们的方法可以在图像网络数据集中产生高质量的对抗性例子。