Revisiting AUC-Oriented Adversarial Training with Loss-Agnostic Perturbations

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 12; pp. 1 - 18
• Main Authors: Yang, Zhiyong, Xu, Qianqian, Hou, Wenzheng, Bao, Shilong, He, Yuan, Cao, Xiaochun, Huang, Qingming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial Learning; Algorithms; AUC Optimization; Linear programming; Machine learning; Machine learning algorithms; Optimization; Perturbation; Perturbation methods; Receivers; Training
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2023.3303934

The Area Under the ROC curve (AUC) is a popular metric for long-tail classification. Many efforts have been devoted to AUC optimization methods in the past decades. However, little exploration has been done to make them survive adversarial attacks. Among the few exceptions, AdAUC presents an early trial for AUC-oriented adversarial training with a convergence guarantee. This algorithm generates the adversarial perturbations globally for all the training examples. However, it implicitly assumes that the attackers must know in advance that the victim is using an AUC-based loss function and training technique, which is too strong to be met in real-world scenarios. Moreover, whether a straightforward generalization bound for AdAUC exists is unclear due to the technical difficulties in decomposing each adversarial example. By carefully revisiting the AUC-orient adversarial training problem, we present three reformulations of the original objective function and propose an inducing algorithm. On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods. 2) AUC-oriented AT does have an explicit error bound to ensure its generalization ability. 3) One can construct a fast SVRG-based gradient descent-ascent algorithm to accelerate the AdAUC method. Finally, the extensive experimental results show the performance and robustness of our algorithm in five long-tail datasets. The code is available at https://github.com/statusrank/AUC-Oriented-Adversarial-Training