arXiv AI July 28, 2026 By Ali Borji

A New Kind of Adversarial Example: Measuring the Human-Model Gap, and Its Relationship to OOD Detection

arXiv:2607. 22722v1 Announce Type: cross Abstract: Almost all adversarial attacks add an imperceptible perturbation to fool a model.

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

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 11

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

arXiv:2606. 12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks.

By Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul

safety

arXiv AI

Jun 9

A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers

arXiv:2606. 07593v1 Announce Type: cross Abstract: The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images.

By Hannah Gao (Massachusetts Institute of Technology), Isha Agarwal (Massachusetts Institute of Technology), Dylan Hadfield-Menell (Massachusetts Institute of Technology), Rachel Ma (Massachusetts Institute of Technology)

llms computer-vision fine-tuning multimodal safety

arXiv Machine Learning

Aug 10

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

arXiv:2608. 06674v1 Announce Type: cross Abstract: Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical systems.

By Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando, Harshala Gammulle, Basura Fernando, Sanka Rasnayake, A V Subramanyam, Sridha Sridharan, Clinton Fookes

llms computer-vision benchmarks safety

arXiv AI

1d ago

Fast Test-Time Refinement for Robust Learned Image Compression

arXiv:2608. 15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings.

By Jiaming Liang, Chi-Man Pun, Weisi Lin

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

Related stories

Binary Iterative Method for Non-targeted Adversarial Attack

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

Fast Test-Time Refinement for Robust Learned Image Compression

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