78 citations · 139 across the 10 of their papers we have counts for
10 papers
DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models
Yingqian Cui, Jie Ren, Han Xu +5
Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images. A large community of GDMs has naturally emerged, furthe…
Sharpness-Aware Data Poisoning Attack
Pengfei He, Han Xu, Jie Ren +4
Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' trai…
Transferable Unlearnable Examples
Jie Ren, Han Xu, Yuxuan Wan +3
With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parti…
Towards Fair Classification against Poisoning Attacks
Han Xu, Xiaorui Liu, Yuxuan Wan +1
Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classificatio…
Probabilistic Categorical Adversarial Attack & Adversarial Training
Han Xu, Pengfei He, Jie Ren +4
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples wi…
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
Han Xu, Hao Qi, Kunyao Wang +8
Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking,ranking, respectively. Constrained by strict requirements on onlin…
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Yuxuan Wan, Han Xu, Xiaorui Liu +3
Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is s…
Enhancing Adversarial Training with Feature Separability
Yaxin Li, Xiaorui Liu, Han Xu +2
Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a countermeasure, adversarial training aims to achieve robustness based on the min-max optimization problem and…
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
Yaxin Li, Wei Jin, Han Xu +1
DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10…
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
Wei Jin, Yaxin Li, Han Xu +4
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the i…