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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
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“New attacks that bypass existing defenses frequently arise, a phenomenon well-documented in computer vision (Athalye et al., 2018;Tramèr et al., 2020;Yu et al., 2021;Carlini and Wagner, 2017).”
From Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents (2025) · cites this paper as Carlini and Wagner, 2017
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602
Explaining Explanations: An Overview of Interpretability of Machine Learning
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Adversarial Examples: Attacks and Defenses for Deep Learning
Yuan, X., He, P., Zhu, Q., & Li, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886017 · Cited by 1,733
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Metadata
- Authors
- Carlini, N., & Wagner, D.
- Affiliations
- University of California, Berkeley, Berkeley, CA, USA
- Venue
- Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security
- Publisher
- ACM
- Published
- 2017-11-03
- Pages
- 3-14
- Cited by
- 966
- References
- 7
- Funders
- Qualcomm
- AFOSR (MURI award FA9550-12-1-0040)
- Intel (ISTC for Secure Computing)
- Hewlett Foundation (Center for Long-Term Cybersecurity)
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