Efficient Robustness Evaluation via Constraint Relaxation

The study of enhancing model robustness against adversarial examples has become increasingly critical in the security of deep learning, leading to the development of numerous adversarial defense techniques. While these defense methods have shown promise in mitigating the impact of adversarial perturbations, evaluating their effectiveness remains a critical challenge. The recently introduced AutoAttack technique has been recognized as a standardized method for assessing model robustness. However, the computational demands of the AutoAttack method significantly limits its applicability, underscoring the urgent need for efficient evaluation techniques. To address this challenge, we propose a novel and efficient evaluation framework based on strategic constraint relaxation. Our key insight is that temporarily expanding the adversarial perturbation bounds during the attack process can help discover more effective adversarial examples. Based on this insight, we develop the Constraint Relaxation Attack (CR Attack) method, which systematically relaxes and resets perturbation constraints during optimization. Extensive experiments on 105 robust models show that CR Attack outperforms AutoAttack in both attack success rate and efficiency, reducing forward and backward propagation time by 38.3× and 15.9× respectively. Through comprehensive analysis, we validate that the constraint relaxation mechanism is crucial for the method's effectiveness.