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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)Patch-based Defenses against Web Fingerprinting Attacks., , , and . AISec@CCS, page 97-109. ACM, (2021)Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural Networks., , , , , and . CoRR, (2019)Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks., , , and . USENIX Security Symposium, page 3575-3592. USENIX Association, (2022)Fawkes: Protecting Privacy against Unauthorized Deep Learning Models., , , , , and . USENIX Security Symposium, page 1589-1604. USENIX Association, (2020)Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models., , , , , and . USENIX Security Symposium, page 2187-2204. USENIX Association, (2023)Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?, , , , , , and . CoRR, (2024)Traceback of Data Poisoning Attacks in Neural Networks., , , and . CoRR, (2021)Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models., , , , and . CoRR, (2023)Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks., , , , , and . USENIX Security Symposium, page 2117-2134. USENIX Association, (2022)