arXiv AI July 14, 2026 By Syed Irfan Ali Meerza, Oktay Ozturk, Amir Sadovnik, Jian Liu

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders

arXiv:2607. 10580v1 Announce Type: cross Abstract: AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthorized facial recognition and targeted advertising.

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

arXiv AI

Jun 11

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

arXiv:2506. 03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications.

By Jia Fu, Yongtao Wu, Yihang Chen, Kunyu Peng, Xiao Zhang, Volkan Cevher, Sepideh Pashami, Anders Holst

llms rag diffusion multimodal safety

arXiv AI

Aug 12

Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry

arXiv:2505. 22839v2 Announce Type: replace-cross Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models.

By Liu Yuezhang, Xue-Xin Wei

diffusion safety

arXiv Machine Learning

Jun 11

Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks

arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.

By Omid Ahmadieh, Nima Karimian

llms rag diffusion robotics fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 18

RUB: Evaluating Residual Knowledge in Unlearned Models

arXiv:2504. 14798v2 Announce Type: replace Abstract: Machine Unlearning (MUL) has emerged as a key mechanism for privacy protection and content regulation, yet current techniques often fail to guarantee the complete removal of sensitive information.

By Hao Xuan, Xingyu Li

diffusion benchmarks safety

arXiv AI

Jul 29

I2VShield: An Efficient Proactive Defense Framework against DiT-based Image-to-Video Models

arXiv:2607. 25522v1 Announce Type: cross Abstract: The rapid advancement of video generation models has led to the increasing misuse of image-to-video (I2V) models.

By Yimao Guo, Zuomin Qu, Wei Lu

llms diffusion multimodal safety

Hugging Face Trending Papers

2d ago

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.

llms diffusion benchmarks safety

Related stories

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry

Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks

RUB: Evaluating Residual Knowledge in Unlearned Models

I2VShield: An Efficient Proactive Defense Framework against DiT-based Image-to-Video Models

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer