Transferable Adversarial Attack based on Integrated Gradients
This work addresses the vulnerability of deep neural networks to adversarial attacks, offering an incremental improvement in transferability for security applications.
The authors tackled the problem of creating highly transferable adversarial examples for black-box attacks on deep neural networks by proposing TAIG, a new algorithm that integrates three common approaches into a single term, and demonstrated that it outperforms state-of-the-art methods in experiments.
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