Learning from Peers: Collaborative Ensemble Adversarial Training

• URL: http://arxiv.org/abs/2509.00089v1
• Date: Wed, 27 Aug 2025 13:10:40 GMT
• Title: Learning from Peers: Collaborative Ensemble Adversarial Training
• Authors: Li Dengjin, Guo Yanming, Xie Yuxiang, Li Zheng, Chen Jiangming, Li Xiaolong, Lao Mingrui,
• Abstract summary: We propose a novel yet efficient Collaborative Ensemble Adversarial Training (CEAT) to highlight the cooperative learning among sub-models in the ensemble.<n>CEAT is model-agnostic, which can be seamlessly adapted into various ensemble methods with flexible applicability.
• Score: 1.805627331168865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensemble Adversarial Training (EAT) attempts to enhance the robustness of models against adversarial attacks by leveraging multiple models. However, current EAT strategies tend to train the sub-models independently, ignoring the cooperative benefits between sub-models. Through detailed inspections of the process of EAT, we find that that samples with classification disparities between sub-models are close to the decision boundary of ensemble, exerting greater influence on the robustness of ensemble. To this end, we propose a novel yet efficient Collaborative Ensemble Adversarial Training (CEAT), to highlight the cooperative learning among sub-models in the ensemble. To be specific, samples with larger predictive disparities between the sub-models will receive greater attention during the adversarial training of the other sub-models. CEAT leverages the probability disparities to adaptively assign weights to different samples, by incorporating a calibrating distance regularization. Extensive experiments on widely-adopted datasets show that our proposed method achieves the state-of-the-art performance over competitive EAT methods. It is noteworthy that CEAT is model-agnostic, which can be seamlessly adapted into various ensemble methods with flexible applicability.

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