arXiv AI
Jul 10
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
arXiv:2607. 07922v1 Announce Type: cross Abstract: Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.
Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
The paper investigates why saliency maps of adversarially‑trained neural networks appear sparse. It provides a theoretical explanation for two‑layer ReLU networks, showing that as data size and neuron count grow, minimizers converge to a Bayes classifier with minimal gradient and Barron norm. The sparsity arises because α−−‑training with λ−1 attacks yields anisotropic gradient norms that favor axis‑aligned, sparse gradients, a claim supported by experimental evaluation of gradient α‑norms and sparsity thresholds.
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arXiv Machine Learning
1d ago
Estimating Implicit Regularization in Deep Learning
arXiv:2605.05436v2 Announce Type: replace-cross Abstract: Deep learning systems are known to exhibit implicit regularization (alt. implicit bias), favoring simple solutions instead of merely minimizi...
arXiv AI
Jun 4
Efficient Adversarial Attacks on High-dimensional Offline Bandits
arXiv:2602. 01658v2 Announce Type: replace-cross Abstract: Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large language models, by efficiently identifying top-performing candidates without exhaustive comparisons.
arXiv AI
Aug 12
Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry
arXiv:2505. 22839v2 Announce Type: replace-cross Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models.
arXiv AI
Sep 18
Exploring Sparsity and Smoothness of Arbitrary Lp Norms in Adversarial Attacks
The paper investigates how the choice of the π parameter in λπ norm-constrained adversarial attacks influences the sparsity and smoothness of the perturbations. By applying two established sparsity metrics and introducing three new smoothness measures—including one based on first-order Taylor approximations—the authors perform extensive experiments on real-world image datasets and various neural network architectures. Their results indicate that λρ norms with π values between 1.3 and 1.5 consistently provide the best balance between sparsity and smoothness, challenging the common use of λ1 or λ2 norms.
arXiv AI
6d ago
Sparse Fine-Tuning for Parameter-Efficient Adversarial Training
The paper introduces Sparse Adversarial Fine‑Tuning (SAFT), a method that identifies a small set of Robustness‑Critical Parameters (RCPs) through adversarial gradient saliency and updates only these parameters during adversarial training. By focusing on this sparse subspace, SAFT achieves higher clean and robust accuracy than LoRA‑based parameter‑efficient methods and matches or surpasses full‑parameter adversarial training while training only about 5% of the parameters. The approach is validated across multiple architectures and datasets, demonstrating its effectiveness in reducing memory cost without sacrificing robustness.
arXiv Machine Learning
Jul 7
Binary Iterative Method for Non-targeted Adversarial Attack
arXiv:2607. 04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models.
arXiv Machine Learning
Aug 4
DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment
arXiv:2511. 13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions.
arXiv Machine Learning
Oct 28
Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection
The paper introduces Augmentation-Guided Adversarial Training (AGAT), a selective adversarial training method that uses augmentation-induced local variation to identify unstable samples for training. AGAT employs Augmentation-based Local Stability (ALS) to rank samples based on the normalized change in model logits between an input and its label-preserving augmented version. Experiments on MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet-200 show that AGAT achieves comparable or higher robust accuracy while applying adversarial supervision to only 20‑30% of training samples per minibatch, and it requires less training time than other adversarial training baselines.
arXiv AI
Jun 29
Improving Adversarial Robustness via Activation Amplification and Attenuation
arXiv:2606. 27784v1 Announce Type: cross Abstract: The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks.
arXiv Machine Learning
Jul 21
Effects of width-dependent model hyperparameters and $\ell_2$-regularization on the loss landscape of two-layer ReLU networks
arXiv:2607. 16720v1 Announce Type: new Abstract: Understanding deep neural networks remains a central challenge in machine learning.
arXiv AI
Jul 9
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
arXiv:2607. 06592v1 Announce Type: cross Abstract: Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their applicability in real-world scenarios.