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20242026
most citedStegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV20263 cited

StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting

Guoqing Ma, Xun Lin, Hui Ma +6

Most existing Face Forgery Detection (FFD) models assume access to raw face images. In practice, under a client-server framework, private facial data may be intercepted during tran…

cs.CV2025

Learning Representation and Synergy Invariances: A Povable Framework for Generalized Multimodal Face Anti-Spoofing

Xun Lin, Shuai Wang, Yi Yu +6

Multimodal Face Anti-Spoofing (FAS) methods, which integrate multiple visual modalities, often suffer even more severe performance degradation than unimodal FAS when deployed in un…

cs.CR2024

Towards Physical World Backdoor Attacks against Skeleton Action Recognition

Qichen Zheng, Yi Yu, Siyuan Yang +3

Skeleton Action Recognition (SAR) has attracted significant interest for its efficient representation of the human skeletal structure. Despite its advancements, recent studies have…

cs.CR2024

Unlearnable Examples Detection via Iterative Filtering

Yi Yu, Qichen Zheng, Siyuan Yang +6

Deep neural networks are proven to be vulnerable to data poisoning attacks. Recently, a specific type of data poisoning attack known as availability attacks has led to the failure…

cs.CR2024

Semantic Deep Hiding for Robust Unlearnable Examples

Ruohan Meng, Chenyu Yi, Yi Yu +3

Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from u…