Shape Matters: Deformable Patch Attack

1 code implementation • European Conference on Computer Vision 2022 • Zhaoyu Chen, Bo Li, Shuang Wu, Jianghe Xu, Shouhong Ding, Wenqiang Zhang

Though deep neural networks (DNNs) have demonstrated excellent performance in computer vision, they are susceptible and vulnerable to carefully crafted adversarial examples which can mislead DNNs to incorrect outputs.

Federated Learning with Label Distribution Skew via Logits Calibration

2 code implementations • 1 Sep 2022 • Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu

Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.

Federated Learning

Towards Practical Certifiable Patch Defense with Vision Transformer

no code implementations • CVPR 2022 • Zhaoyu Chen, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Wenqiang Zhang

To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS).

Towards Efficient Data Free Black-Box Adversarial Attack

1 code implementation • CVPR 2022 • Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu

The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.

Adversarial Attack