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A Comparative Study of Cross-Model Universal Adversarial Perturbation for Face Forgery.

, , and . VCIP, page 1-5. IEEE, (2022)

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An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification., , , and . IEEE Trans. Biom. Behav. Identity Sci., 1 (1): 42-55 (2019)A Fast and Accurate System for Face Detection, Identification, and Verification., , , , , , , , , and . IEEE Trans. Biom. Behav. Identity Sci., 1 (2): 82-96 (2019)A cooperative image object recognition framework and task offloading optimization in edge computing., , and . J. Netw. Comput. Appl., (2022)Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans., , , , , , , and . IEEE Signal Process. Mag., 35 (1): 66-83 (2018)Lightweight Face Anti-Spoofing Network for Telehealth Applications., , , , , , and . IEEE J. Biomed. Health Informatics, 26 (5): 1987-1996 (2022)Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks., , , , , , , and . Int. J. Comput. Vis., 126 (2-4): 272-291 (2018)Robust Representations for unconstrained Face Recognition and its Applications.. University of Maryland, College Park, MD, USA, (2016)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/19013).Enhancing Real-Time Semantic Segmentation with Textual Knowledge of Pre-Trained Vision-Language Model: A Lightweight Approach., , and . APSIPA ASC, page 551-558. IEEE, (2023)Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition., , , , , , and . CoRR, (2018)Hearing and Seeing Abnormality: Self-Supervised Audio-Visual Mutual Learning for Deepfake Detection., , and . ICASSP, page 1-5. IEEE, (2023)