Aug 2026· Symposium on Designing Interactive Systems· pp. 1-6· 0 citations· 25 references
Abstract
Adversarial training (AT) is a mainstream paradigm for improving the robustness of deep models. However, natural and adversarial examples often exhibit distributional discrepancies in feature space, and fixed-weight contrastive constraints can conflict with classification learning when applied throughout training. To address this issue, we propose Dynamic-weighted Contrastive Adversarial Training (DWCAT). Built on the AT framework, DWCAT treats the natural and adversarial versions of the same image as positive pairs, aligns their representations in the feature space via NT-Xent, and gradually introduces the contrastive term through a staged dynamic weighting scheme, distinct from the fixed-weight strategies commonly adopted in existing contrastive adversarial training methods. Experiments on CIFAR-10 show that DWCAT achieves 82.31% natural accuracy, 51.66% robust accuracy under PGD-10, and a mean robustness score of 52.47%, which is the highest among the compared methods. Ablation studies further confirm the effectiveness of dynamic weighting and the natural-adversarial positive-pair design.
Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are generated for every training sample. Selective adversarial training reduces this cost by perturbing only a subset of samples. How...
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Malik Al-sharaf· FUDMA Journal of Sciences· 0 citations
Unrestricted Adversarial Examples (UAEs) pose a growing security challenge to Deep Neural Networks by introducing substantial, semantically natural modifications to images. While current diffusion-based methods improve the naturalness of UAEs, they suffer from two key limitations: an underutilization of the diffusion m...
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Deep neural networks (DNNs) are vulnerable to small input perturbations, resulting in instability and adversarial susceptibility. While adversarial training methods such as PGD-AT and TRADES improve robustness by generating worst-case adversarial examples, they do not explicitly regulate the model's local smoothness. I...
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