TL;DR
A robustness-oriented training framework that integrates Mask-Guided Adversarial Mixup (MGAM) and Adaptive Timescale Exponential Moving Average (AT-EMA) that provides a practical data-regularization strategy for improving training stability in adversarial learning is proposed.
A Confusion Geometry Rebalancing method for long tail adversarial training, a plug in framework that leverages directed robust errors as training signals and couples feedback weighted robust optimization with graph guided margin correction, thereby boosting the robustness of vulnerable classes and sharpening the critic...
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 c...
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.
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.
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 a...
Empirical support is provided for the utility of structure-aware perturbation refinement in improving black-box adversarial transferability across heterogeneous visual architectures.
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