This paper reveals that the iterative optimization process tends to over-specialize adversarial perturbations to the local gradient characteristics of the surrogate model, thereby hindering their transferability to other models, and proposes a novel attack method called Local Perturbation Augmentation Attack.
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.
This work introduces Adversarial Importance Sampling (Advis), a method that uses importance sampling over trajectories from standard training to estimate and optimize verifiable worst-case returns and introduces advrl, a modular PyTorch library that provides clean, single-file implementations of existing robustness met...
Amine Andam, Jamal Bentahar, M. Hedabou· 0 citations
Empirical support is provided for the utility of structure-aware perturbation refinement in improving black-box adversarial transferability across heterogeneous visual architectures.
Qi-Rui Lu, Liansong Zong, Fu-Ran Liu et al.· Neural Networks· 0 citations
Adversarial attacks pose significant challenges to the security and robustness of deep-learning models. Stochastic neural networks (SNNs) have shown promising effectiveness in improving robustness by injecting stochastic noise into model activations, features, or weights. However, most existing SNN-based defenses rely...
Rui Zhou, Hao Yang, Wen-Xu Wang et al.· IEEE Transactions on Neural...· 0 citations
The Enhanced Deformation Attack (EDA), which estimates attack gradients over stochastically transformed views of the current adversarial image, achieves the highest mean attack success rate (ASR) on ViT targets for all four CNN sources, outperforming the strongest source-specific baseline.
To mitigate the vulnerability of deep neural networks in the face of adversarial attacks, a variety of defense strategies have been proposed in recent years. Adversarially robust distillation provides an effective approach by transferring knowledge from a robust teacher model to a student model. However, most existing...
Rihao Li, Ran Wang, Meng Hu· 2026 International Symposium...· 0 citations
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