1 citations · 3 across the 6 of their papers we have counts for
6 papers
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li +6
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that…
When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining
Zhihao Li, Gezheng Xu, Jiale Cai +5
Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlyin…
Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model
Hang Zhou, Jiale Cai, Yuteng Ye +5
A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recove…
Attacking Transformers with Feature Diversity Adversarial Perturbation
Chenxing Gao, Hang Zhou, Junqing Yu +4
Understanding the mechanisms behind Vision Transformer (ViT), particularly its vulnerability to adversarial perturba tions, is crucial for addressing challenges in its real-world a…
Fine-grained Appearance Transfer with Diffusion Models
Yuteng Ye, Guanwen Li, Hang Zhou +7
Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coher…
Progressive Text-to-Image Diffusion with Soft Latent Direction
YuTeng Ye, Jiale Cai, Hang Zhou +6
In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose e…