Reliable Evaluation of Adversarial Transferability

Understand

Adversarial examples (AEs) with small adversarial perturbations can mislead deep neural networks (DNNs) into wrong predictions.

• The AEs created on one DNN can also fool another DNN.
• Over the last few years, the transferability of AEs has garnered significant attention as it is a crucial property for facilitating black-box attacks.
• Many approaches have been proposed to improve adversarial transferability.