| # Qidong Huang | |
| Building No.7, USTC West CampusHefei, Anhui, China | |
| Ph.D, University of Science and Technology of China | |
| H (+86) 13085060686 | |
| B [email protected] | |
| # Short Biography | |
| Qidong Huang is a PhD student at University of Science and Technology of China. He has published more than 7 papers at top1-tier conferences and journals, such as CVPR/ICCV/AAAI/TIP/TCSVT. His research interests focus on vision transfer learning (e.g., prompt learning for vision pretrained models) and artificial intelligence security (e.g., adversarial examples and anti-DeepFake). He is the reviewer of many top conferences (including CVPR, ICCV, ECCV) and top journals (TNNLS, PR). | |
| # Education | |
| |09/2020–present|PhD of Cyberspace Security, University of Science and Technology of China, Hefei, China, CAS Key Laboratory of Electromagnetic Space Information. Supervised by Prof. Weiming Zhang.| | |
| |---|---| | |
| |09/2016–06/2020|Bachelor of Information Security, School of Information Science and Technology, University of Science and Technology of China, Hefei, China.| | |
| # Skills | |
| - Expertise in vision prompt learning: I have been researching the prompt learning for large-scale vision pretrained models and published one paper on top-tier computer vision conferences, in which I propose DAM-VP, a data diversity-aware method for efficient and adaptive vision prompt learning. This work alleviates the mismatch between vision prompts and downstream data diversity. | |
| - Expertise in artificial intelligence security: I have been studying artificial intelligence security since 2020, including adversarial attack&defense and anti-DeepFake. For adversarial attack, I propose SI-Adv, a shape-invariant attack for 3D point cloud recognition which great boosts the imperceptibility of adversarial examples. For adversarial defense, I propose a contrastive adversarial training framework for robust point cloud recognition named PointCAT. Besides, our work for improving adversarial robustness of masked autoencoders has been recently accepted by ICCV 2023. For anti-DeepFake, we are the first to propose the concept of “initiative defense” against DeepFakes by proactively protecting users’ facial privacy before the manipulation, unlike previous ex-post countermeasures like DeepFake detection. | |
| # Publications (First Author) | |
| Qidong Huang, Xiaoyi Dong, Dongdong Chen, Yinpeng Chen, Lu Yuan, Gang Hua, Weiming Zhang, Nenghai Yu. Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting. International Conference on Computer Vision (ICCV), 2023. | |
| Qidong Huang, Xiaoyi Dong, Dongdong Chen, Weiming Zhang, Feifei Wang, Gang Hua, Nenghai Yu. Diversity-Aware Meta Visual Prompting. Conference on Computer Vision and Pattern Recognition (CVPR), 2023. | |
| Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Nenghai Yu. Shape-invariant 3D Adversarial Point Clouds. Conference on Computer Vision and Pattern Recognition (CVPR), 2022. | |
| --- | |
| # Publications | |
| Qidong Huang*, Jie Zhang*, Wenbo Zhou, Weiming Zhang, Nenghai Yu. Initiative Defense against Facial Manipulation. AAAI Conference on Artificial Intelligence (AAAI), 2021. (*Qidong Huang and Jie Zhang contribute equally.) | |
| Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Kui Zhang, Gang Hua, Nenghai Yu. PointCAT : Contrastive Adversarial Training for Robust Point Cloud Recognition. IEEE Transactions on Image Processing (TIP), Major Revision. | |
| Kui Zhang, Hang Zhou, Jie Zhang, Qidong Huang, Weiming Zhang, Nenghai Yu. Ada3Diff : Defending against 3D Adversarial Point Clouds via Adaptive Diffusion. Under Review | |
| Han Fang, Dongdong Chen, Qidong Huang, Jie Zhang, Zehua Ma, Weiming Zhang* and Nenghai Yu. Deep Template-based Watermarking. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020. | |
| Jie Zhang, Dongdong Chen, Qidong Huang, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, Nenghai Yu. Poison ink : Robust and invisible backdoor attack. IEEE Transactions on Image Processing (TIP), 2022. | |
| # Services | |
| - Reviewer for CVPR 2022, 2023 | |
| - Reviewer for ICCV 2023 | |
| - Reviewer for ECCV 2022 | |
| - Reviewer for ICPR 2022 | |
| - Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS) | |
| - Reviewer for Pattern Recognition (PR) | |
| # Awards & Honors | |
| 2021 China National Scholarship |