Transferable Adversarial Attack based on Integrated Gradients

Understand

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.