collaborators

8 papers

cs.SD2026

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…

cs.CV2026

IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations

Weichen Liu, Yixin Yang, Changsheng Chen +1

Suspect face generation remains a technical challenge in crime investigations. Traditional sketch-drawing workflows suffer from low efficiency and quality, while diffusion-based ap…

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.CV2025

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution

Cecelia Soh, Rizhao Cai, Monalisha Paul +2

Skin health and disease resistance are closely linked to the skin barrier function, which protects against environmental factors and water loss. Two key physiological indicators ca…

cs.CV2025

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…

cs.CV2025

ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack

Rongxuan Peng, Shunquan Tan, Chenqi Kong +3

Parameter-efficient fine-tuning (PEFT) has emerged as a popular strategy for adapting large vision foundation models, such as the Segment Anything Model (SAM) and LLaVA, to downstr…