arXiv AI June 30, 2026 By Maher Boughdiri, Mounira Msahli, Albert Bifet

AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors

arXiv:2606. 28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

benchmarks safety

arXiv AI

Aug 11

Multi-Task Consistency-based Detection of Adversarial Attacks

arXiv:2608. 07750v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems.

By Cong Chen, Jean-Philippe Monteuuis, Jonathan Petit

agents computer-vision robotics safety

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.

By Naman Goyal, Milan Chaudhari

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

fine-tuning benchmarks safety

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 AI

Jul 1

Improving Certified Robustness via Adversarial Distillation

arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.

By Matteo Melis, Jesus Martinez Del Rincon, Vishal Sharma

efficiency benchmarks safety

Related stories

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

Multi-Task Consistency-based Detection of Adversarial Attacks

Binary Iterative Method for Non-targeted Adversarial Attack

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

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

Improving Certified Robustness via Adversarial Distillation