  Although deep neural networks (DNNs) have achieved success in many
application fields, it is still vulnerable to imperceptible adversarial
examples that can lead to misclassification of DNNs easily. To overcome this
challenge, many defensive methods are proposed. Indeed, a powerful adversarial
example is a key benchmark to measure these defensive mechanisms. In this
paper, we propose a novel method (TEAM, Taylor Expansion-Based Adversarial
Methods) to generate more powerful adversarial examples than previous methods.
The main idea is to craft adversarial examples by minimizing the confidence of
the ground-truth class under untargeted attacks or maximizing the confidence of
the target class under targeted attacks. Specifically, we define the new
objective functions that approximate DNNs by using the second-order Taylor
expansion within a tiny neighborhood of the input. Then the Lagrangian
multiplier method is used to obtain the optimize perturbations for these
objective functions. To decrease the amount of computation, we further
introduce the Gauss-Newton (GN) method to speed it up. Finally, the
experimental result shows that our method can reliably produce adversarial
examples with 100% attack success rate (ASR) while only by smaller
perturbations. In addition, the adversarial example generated with our method
can defeat defensive distillation based on gradient masking.
