Hugging Face Trending Papers June 11, 2026

Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition

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Adversarial attacks on skeletal human action recognition have received significant attention. However, existing methods typically introduce noise-like perturbations that degrade motion quality post-attack, and thereby are inherently perceptible with recent advancements in S-HAR systems.

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Hugging Face Trending Papers

2d ago

Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples

The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images.

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 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 AI

Jun 2

Calibrating Uncertainty for Zero-Shot Adversarial CLIP

arXiv:2512. 12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks.

By Wenjing Lu, Zerui Tao, Yuning Qiu, Dongping Zhang, Yang Yang, Qibin Zhao

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 21

Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks

arXiv:2603. 22590v2 Announce Type: replace Abstract: With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become highly relevant.

By Mat\'ias Pizarro, Raghavan Narasimhan, Jonas Killian, Asja Fischer

agents multimodal safety

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

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Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples

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

Binary Iterative Method for Non-targeted Adversarial Attack

Calibrating Uncertainty for Zero-Shot Adversarial CLIP

Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification