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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.

, , , , , , and . IEEE Symposium on Security and Privacy, page 707-723. IEEE, (2019)

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Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks., , , , , and . USENIX Security Symposium, page 2117-2134. USENIX Association, (2022)Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization., , , , , , and . CHI, page 493. ACM, (2018)Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models., , , , and . CoRR, (2023)Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN Models., , , , and . CCS, page 2611-2625. ACM, (2022)SoK: Anti-Facial Recognition Technology., , , and . SP, page 864-881. IEEE, (2023)Penny Auctions are Predictable: Predicting and Profiling User Behavior on DealDash., , , , and . HT, page 123-127. ACM, (2018)Understanding Implosion in Text-to-Image Generative Models., , , , and . CoRR, (2024)Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks., , , , , , and . IEEE Symposium on Security and Privacy, page 707-723. IEEE, (2019)Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural Networks., , , , , and . CoRR, (2019)Patch-based Defenses against Web Fingerprinting Attacks., , , and . AISec@CCS, page 97-109. ACM, (2021)