Short Overview: SoK: Neural Network Extraction Through Physical Side Channels Péter Horváth, Dirk Lauret, Zhuoran Liu, and Lejla Batina, ...

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USENIX Security '24 - SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models

USENIX Security '24 - SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models

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USENIX Security '24 - Uncovering the Limits of Machine Learning for Automatic Vulnerability...

USENIX Security '24 - Uncovering the Limits of Machine Learning for Automatic Vulnerability...

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USENIX Security '24 - How Does a Deep Learning Model Architecture Impact Its Privacy?...

USENIX Security '24 - How Does a Deep Learning Model Architecture Impact Its Privacy?...

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USENIX Security '21 - Systematic Evaluation of Privacy Risks of Machine Learning Models

USENIX Security '21 - Systematic Evaluation of Privacy Risks of Machine Learning Models

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USENIX Security '24 - Towards More Practical Threat Models in Artificial Intelligence Security

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USENIX Security '24 - SoK: Neural Network Extraction Through Physical Side Channels

USENIX Security '24 - SoK: Neural Network Extraction Through Physical Side Channels

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USENIX Security '24 - INSIGHT: Attacking Industry-Adopted Learning Resilient Logic Locking...

USENIX Security '24 - INSIGHT: Attacking Industry-Adopted Learning Resilient Logic Locking...

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USENIX Security '24 - Quantifying Privacy Risks of Prompts in Visual Prompt Learning

USENIX Security '24 - Quantifying Privacy Risks of Prompts in Visual Prompt Learning

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USENIX Security '20 - Exploring Connections Between Active Learning and Model Extraction

USENIX Security '20 - Exploring Connections Between Active Learning and Model Extraction

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USENIX Security '24 - MD-ML: Super Fast Privacy-Preserving Machine Learning for Malicious...

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