99 citations · 635 across the 57 of their papers we have counts for
16 papers · 1 filter
Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks
Zhaoyu Chen, Bo Li, Kaixun Jiang +3
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and id…
Contrastive Pseudo Learning for Open-World DeepFake Attribution
Zhimin Sun, Shen Chen, Taiping Yao +4
The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken es…
Privacy-Preserving Face Recognition Using Random Frequency Components
Yuxi Mi, Yuge Huang, Jiazhen Ji +5
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. Thi…
Continual Face Forgery Detection via Historical Distribution Preserving
Ke Sun, Shen Chen, Taiping Yao +3
Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall sh…
Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
Qiang Wang, Junlong Du, Ke Yan +1
The Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training and has applied to many downstream tasks. We explore the ada…
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Lei Shen, Jianlong Jin, Ruixin Zhang +7
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets li…
Query-Efficient Decision-based Black-Box Patch Attack
Zhaoyu Chen, Bo Li, Shuang Wu +2
Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce…
Towards General Visual-Linguistic Face Forgery Detection
Ke Sun, Shen Chen, Taiping Yao +4
Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which us…
Towards Generalizable Data Protection With Transferable Unlearnable Examples
Bin Fang, Bo Li, Shuang Wu +4
Artificial Intelligence (AI) is making a profound impact in almost every domain. One of the crucial factors contributing to this success has been the access to an abundance of high…
Re-thinking Data Availablity Attacks Against Deep Neural Networks
Bin Fang, Bo Li, Shuang Wu +3
The unauthorized use of personal data for commercial purposes and the clandestine acquisition of private data for training machine learning models continue to raise concerns. In re…
Content-based Unrestricted Adversarial Attack
Zhaoyu Chen, Bo Li, Shuang Wu +3
Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photoreal…
Instance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +4
Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain…
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
Zexin Li, Bangjie Yin, Taiping Yao +4
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information…
DistilPose: Tokenized Pose Regression with Heatmap Distillation
Suhang Ye, Yingyi Zhang, Jie Hu +6
In the field of human pose estimation, regression-based methods have been dominated in terms of speed, while heatmap-based methods are far ahead in terms of performance. How to tak…
Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search
Guanshuo Wang, Fufu Yu, Junjie Li +2
Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images. Recent Vision-Language Pre-training (VLP) models can bring…
Delving into the Adversarial Robustness of Federated Learning
Jie Zhang, Bo Li, Chen Chen +4
In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largel…