  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
