arXiv Machine Learning July 7, 2026 By Naman Goyal, Milan Chaudhari

Binary Iterative Method for Non-targeted Adversarial Attack

Read the original on arXiv Machine Learning →

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.

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arXiv Machine Learning

Jun 2

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.

By Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno

benchmarks safety

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.

By Ci Lin, Tet Yeap, Iluju Kiringa

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arXiv Machine Learning

Aug 3

Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations

arXiv:2601. 14300v4 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models.

By Jun Liu, Leo Yu Zhang, Fengpeng Li, Isao Echizen, Jiantao Zhou

computer-vision benchmarks safety

arXiv Machine Learning

Jul 21

A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic

arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.

By Khushnaseeb Roshan

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arXiv Machine Learning

Sep 22

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

The paper introduces RIBA, a reinforcement‑learning inspired black‑box adversarial attack that generates perturbations for neural networks with fewer queries than existing methods. RIBA achieves a 25.4% reduction in median queries on ResNet‑18/Cifar10 and a 22.5% reduction on Vit‑B/16/ImageNet, while matching white‑box attack performance on an adversarially trained model.

By Florian Krone, Elena Hoemann, Sven Hallerbach

llms computer-vision reinforcement-learning safety

arXiv Machine Learning

Jun 2

Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness

arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.

By Kai Wang

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