English(EN)Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers
机器学习研究探索对抗性训练以增强鲁棒性 · 跟踪 5 个来源
作者PulseAugur 编辑部·[6 个来源]·
该机器学习论文集探讨了对抗性训练的各个方面。研究调查了对抗性噪声如何影响分类器鲁棒性，比较了不同的分布式训练算法及其在逃避局部最小值方面的效率。此外，一篇论文引入了一种对抗性训练的概率方法，另一篇则提出了一种包含字典结构的新型网络架构，以增强鲁棒性和泛化能力。这些研究共同旨在提高机器学习模型抵御复杂攻击的弹性。
AI
arXiv:2610.03498v1 Announce Type: cross Abstract: Adversarial patches can disrupt Vision-Language-Action (VLA) models by manipulating visual observations, leading to failures in robot control. However, it remains poorly understood which internal mechanisms underlie these failures…
arXiv cs.LG
TIER_1English(EN)·Parker Hummel (Minot State University), Ryne Skabo (Minot State University), Muhammad Abusaqer (Minot State University)·
arXiv:2610.00136v1 Announce Type: cross Abstract: This paper presents a reproducible, educational study of evasion attacks in image classification and text classification. A compact convolutional network trained on MNIST reached 98.63% clean test accuracy and was evaluated under …
arXiv cs.LG
TIER_1English(EN)·Ying Cao, Kun Yuan, Ali H. Sayed·
arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different distributed adversarial training algorithms--inclu…
arXiv:2609.39798v1 Announce Type: cross Abstract: Building on a probabilistic perspective in which adversarial examples arise from the overlap between a distance-based distribution $p_{\mathrm{dis}}$ and a victim-classifier-induced distribution $p_{\mathrm{vic}}$, we start from a…
arXiv:2502.00834v2 Announce Type: replace Abstract: This work investigates a novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models. Specifically, our study surprisingly reveals that existing dic…
arXiv stat.ML
TIER_1English(EN)·Elis Stefansson, David V\"avinggren, Ant\^onio H. Ribeiro·
arXiv:2609.39449v1 Announce Type: new Abstract: Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged as a principled framework for analyzing robustness and generalization. In particula…
