Hugging Face Trending Papers July 6, 2026

Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets

Read the original on Hugging Face Trending Papers →

Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave like backdoor-like triggers without being maliciously inserted.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

safety

arXiv AI

Jul 8

Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets

arXiv:2607. 05516v1 Announce Type: cross Abstract: Model-specific adversarial attacks have been extensively studied.

By Paul K. Mandal, Pavan Reddy, Tristan Malatynski

safety

arXiv AI

Aug 18

Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets

arXiv:2607. 05516v2 Announce Type: replace-cross Abstract: Model-specific adversarial attacks have been extensively studied.

By Paul K. Mandal, Pavan Reddy, Tristan Malatynski

safety

arXiv Machine Learning

Jun 3

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.

By Ivan Saboli\'c, Marin Or\v{s}i\'c, Josip \v{S}ari\'c, Sven Lon\v{c}ari\'c

llms fine-tuning multimodal

arXiv Computer Vision

Sep 22

BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models

arXiv:2511.18921v2 Announce Type: replace Abstract: Backdoor attacks undermine the reliability and trustworthiness of machine learning systems by injecting hidden behaviors that can be maliciously ac...

By Juncheng Li, Yige Li, Hanxun Huang, Yunhao Chen, Xin Wang, Yixu Wang, Xingjun Ma, Yu-Gang Jiang

llms nlp multimodal benchmarks safety

arXiv AI

Jun 26

TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models

arXiv:2606. 26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation.

By William Aiken, Paula Branco, Guy-Vincent Jourdan, Iosif-Viorel Onut

diffusion

arXiv Machine Learning

Aug 6

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP

arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.

By Binyan Xu, Xilin Dai, Di Tang, Kehuan Zhang

llms multimodal safety

One email a morning, machine-written

Related stories

Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets

Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models

TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP