4 citations · 13 across the 18 of their papers we have counts for
11 papers · 1 filter
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
Yi Yu, Song Xia, Xun Lin +4
No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and o…
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis
Rizhao Cai, Cecelia Soh, Zitong Yu +3
Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. While recent FAS works are mainly mo…
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…
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…
Controllable and Gradual Facial Blemishes Retouching via Physics-Based Modelling
Chenhao Shuai, Rizhao Cai, Bandara Dissanayake +5
Face retouching aims to remove facial blemishes, such as pigmentation and acne, and still retain fine-grain texture details. Nevertheless, existing methods just remove the blemishe…
Benchmarking Cross-Domain Audio-Visual Deception Detection
Xiaobao Guo, Zitong Yu, Nithish Muthuchamy Selvaraj +3
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Conventional contact-based techniques, like p…
Improving Concept Alignment in Vision-Language Concept Bottleneck Models
Nithish Muthuchamy Selvaraj, Xiaobao Guo, Adams Wai-Kin Kong +1
Concept Bottleneck Models (CBM) map images to human-interpretable concepts before making class predictions. Recent approaches automate CBM construction by prompting Large Language…
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Chenqi Kong, Anwei Luo, Peijun Bao +5
Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expr…
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
Xun Lin, Yi Yu, Song Xia +8
The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding…
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Xun Lin, Shuai Wang, Rizhao Cai +5
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With advancements in sensor manufacture and multi-modal learning techniques,…
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
Fan Zhang, Xiaobao Guo, Xiaojiang Peng +1
Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on…