Research Interests
My research interests include AI security, data privacy, Web3 and blockchain systems, with the focus on AI security and model robustness, data privacy in AI and various fields, Web3 and decentralized identity (DID), blockchain security and system development.
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Recent Related Work:
We introduce a new generative network with separated latent features to construct attacks and defenses for a “smile” detector. |
We propose a new adversarial attack method called GeoFool, GeoFool can generate Common knowledge and individual knowledge. |
We propose a new generative adversarial attack algorithm with graph-convolutional condition GAN, which aims towards realistic and strong adversarial attack against 3D point cloud deep learning model. |
We introduce a DAE-GAN model and a data-augmentation-based training algorithm for detecting abnormal traffic. |
Recent Related Publications:
- IEEE S&P 2023: Leaking Arbitrarily Many Secrets: Any-out-of-Many Proofs and Applications to RingCT Protocols
- ESORICS 2023: n-MVTL Attack: Optimal Transaction Reordering Attack on DeFi
- IEEE/CVF CVPR 2023: StyLess: Boosting the Transferability of Adversarial Examples
- IEEE/CVF CVPR 2023: Physical-World Optical Adversarial Attacks on 3D Face Recognition
- IEEE ICDCS 2022: zkDET: A Traceable Data Exchange Scheme based on Non-Fungible Token and Zero-Knowledge
- ACM Multimedia 2021: Towards Multiple Black-boxes Attack via Adversarial Example Generation Network
- ACM CCS 2019: Power Adjusting and Bribery Racing: Novel Mining Attacks in the Bitcoin System
- IEEE J-SAC 2019: Power Control Identification: A Novel Sybil Attack Detection Scheme in VANETs Using RSSI
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