5 citations · 17 across the 12 of their papers we have counts for
12 papers
SAME: Sample Reconstruction against Model Extraction Attacks
Yi Xie, Jie Zhang, Shiqian Zhao +2
While deep learning models have shown significant performance across various domains, their deployment needs extensive resources and advanced computing infrastructure. As a solutio…
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
Sibo Wang, Jie Zhang, Zheng Yuan +1
Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability,…
Improving Adversarial Transferability by Stable Diffusion
Jiayang Liu, Siyu Zhu, Siyuan Liang +4
Deep neural networks (DNNs) are susceptible to adversarial examples, which introduce imperceptible perturbations to benign samples, deceiving DNN predictions. While some attack met…
Understanding Data Augmentation from a Robustness Perspective
Zhendong Liu, Jie Zhang, Qiangqiang He +1
In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness. Yet, a considerable number of existing methodologies lean heavi…
Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking
Weitao Feng, Jiyan He, Jie Zhang +4
AIGC (AI-Generated Content) has achieved tremendous success in many applications such as text-to-image tasks, where the model can generate high-quality images with diverse prompts,…
Aparecium: Revealing Secrets from Physical Photographs
Zhe Lei, Jie Zhang, Jingtao Li +2
Watermarking is a crucial tool for safeguarding copyrights and can serve as a more aesthetically pleasing alternative to QR codes. In recent years, watermarking methods based on de…