Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness

Abstract

The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness. However, the progress is usually hampered by insufficient robustness evaluations. As the de facto standard to evaluate adversarial robustness, adversarial attacks typically solve an optimization problem of crafting adversarial examples with an iterative process. In this work, we propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the optimizer in adversarial attacks parameterized by a recurrent neural network, which is trained over a class of data samples and defenses to produce effective update directions during adversarial example generation. Furthermore, we develop a model-agnostic training algorithm to improve the generalization ability of the learned optimizer when attacking unseen defenses. Our approach can be flexibly incorporated with various attacks and consistently improves the performance with little extra computational cost. Extensive experiments demonstrate the effectiveness of the learned attacks by MAMA compared to the state-of-the-art attacks on different defenses, leading to a more reliable evaluation of adversarial robustness.

A Roadmap for Big Model

“…They apply AutoAttack on tens of previous defenses and provide a comprehensive leader board. [955] propose MAMA based on training meta optimizers, which is computationally more efficient than AutoAttack with comparable attacking effectiveness. [956] propose the black-box RayS attack, and establish a similar leader board for defenses.…”

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

“…Despite its † Corresponding authors. blooming development [5,27,34], recent research in adversarial machine learning has revealed that face recognition models based on deep neural networks are highly vulnerable to adversarial examples [11,42], leading to serious consequences or security problems in real-world applications.…”