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Preprint Aug 2026

Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training

Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias. Existing methods mitigate this issue by correcting class priors or adapting class wise robust supervision, yet they treat each class in isolation and fail to identify which boundaries drive long tailed collapse. We propose a Confusion Geometry Rebalancing method (CGRm) for long tail adversarial training, a plug in framework that leverages directed robust errors as training signals. CGRm leverages periodic robust evaluations to derive source class loss weights, class wise robust coefficients, and a directed confusion geometry graph. The method then couples feedback weighted robust optimization with graph guided margin correction, thereby boosting the robustness of vulnerable classes and sharpening the critical boundaries that drive long tailed performance degradation. Experiments on long tailed benchmarks show that CGRm achieves consistent robust performance gains over existing methods, with ablations validating the contribution of each component. We provide the code in the supplement.

Mengnan Zhao, Geyong Min, Lihe Zhang et al. · 0 citations
Open access Aug 2026

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data.

Shuhai Zhang, Jiahao Yang, Hui Luo et al. · 0 citations
Preprint Jul 2026

Improving the Robustness/Accuracy Tradeoff Against Adversarial Attacks Using Information Bottleneck Distillation Through Dual Teachers

The proposed method improves classification accuracy on clean samples compared to the original IBD, while maintaining similar accuracy on adversarial samples, and is competitive with state-of-the-art approaches, including the recent dual-teacher distillation framework B-MTARD.

Vincent Ryusuke Takahashi, Yoshinari Takeishi, Jun'ichi Takeuchi et al. · 0 citations
Preprint Aug 2026

Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks

This work proposes BMAT (Bilevel-Minimax Adversarial Transfer), an integrated bottom-up solver that combines a Soft Weight Modulator and an Implicit Gradient Approximator to enable ternary coupling among initialization, surrogate adaptation, and perturbation optimization.

Yaohua Liu, Yifan Guo, Jiaxin Gao · 0 citations

Parameter-Efficient Dual-Loss Adaptation with Logit Divergence: A Unified Approach for Adversarial Example Detection and Robust Inference

D3Adapter is presented, a threat-aware framework that unifies adversarial example detection (AED) and robust inference and scalability across datasets with varying numbers of classes is studied, showing that unified detection and robust inference can be achieved with predictable overhead proportional to the number of adapters.

Zirui Fu, Marco Donato · 0 citations