The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are commonly used to craft adversarial examples. By tightly integrating the three approaches, we propose a new and simple algorithm named Transferable Attack based on Integrated Gradients (TAIG) in this paper, which can find highly transferable adversarial examples for black-box attacks. Unlike previous methods using multiple computational terms or combining with other methods, TAIG integrates the three approaches into one single term. Two versions of TAIG that compute their integrated gradients on a straight-line path and a random piecewise linear path are studied. Both versions offer strong transferability and can seamlessly work together with the previous methods. Experimental results demonstrate that TAIG outperforms the state-of-the-art methods. The code will available at https://github.com/yihuang2016/TAIG

翻译：深神经网络对对抗性实例的脆弱性引起了社区的极大关注。三种方法,即优化标准客观功能、利用关注地图和平滑决定表面,通常用于编织对抗性实例。我们通过严格整合三种方法,提出了一个新的简单算法,即基于集成渐变(TAIG)的可转移攻击(TAIG),这可以发现黑箱攻击的可转移性很强的对抗性例子。与以往使用多种计算术语或与其他方法相结合的方法不同,TAIG将三种方法整合为一个单一术语。两种版本的TAIG,在直线路径上计算其集成梯度,并随机地用片断线性线性路径。两种版本都提供了很强的可转移性,并且可以与以往的方法配合。实验结果表明,TAIG超越了最先进的方法。该代码将在https://github.com/yihwang2016/TAIG上查阅。该代码将在https://github.com/yihwang2016/TAIG上查阅。