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Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models., , , , and . CoRR, (2021)NoiseScope: Detecting Deepfake Images in a Blind Setting., , , , and . ACSAC, page 913-927. ACM, (2020)Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models., , , , and . EuroS&P, page 139-157. IEEE, (2020)T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification., , , , , , , and . USENIX Security Symposium, page 2255-2272. USENIX Association, (2021)Investigating Stateful Defenses Against Black-Box Adversarial Examples., , , , , and . CoRR, (2023)Dispelling Misconceptions and Characterizing the Failings of Deepfake Detection., and . IEEE Secur. Priv., 20 (2): 61-67 (2022)D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles., , , , , and . WACV, page 3800-3810. IEEE, (2024)Towards Adversarially Robust Deepfake Detection: An Ensemble Approach., , , , , and . CoRR, (2022)Deepfake Videos in the Wild: Analysis and Detection., , , , , , , and . WWW, page 981-992. ACM / IW3C2, (2021)GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems., , , , , and . EuroS&P, page 664-683. IEEE, (2022)