Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

With the rapid advancement of diffusion-based image-generative models, the quality of generated images has become increasingly photorealistic. Moreover, with the release of high-quality pre-trained image-generative models, a growing number of users are downloading these pre-trained models to fine-tune them with downstream datasets for various image-generation tasks. However, employing such powerful pre-trained models in downstream tasks presents significant privacy leakage risks. In this paper, we propose the first scores-based membership inference attack framework tailored for recent diffusion models, and in the more stringent black-box access setting. Considering four distinct attack scenarios and three types of attacks, this framework is capable of targeting any popular conditional generator model, achieving high precision, evidenced by an impressive AUC of 0.95.

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No Source Code? No Problem! Twenty Years of Research...

Jack W. Davidson, Professor of Computer Science in the School of Engineering and Applied Science, University of Virginia

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SNITCH: Leveraging IP Geolocation for Active VPN Detection

Tomer Schwartz (Data and Security Laboratory Fujitsu Research of Europe Ltd), Ofir Manor (Data and Security Laboratory Fujitsu Research of Europe Ltd), Andikan Otung (Data and Security Laboratory Fujitsu Research of Europe Ltd)

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Spatial-Domain Wireless Jamming with Reconfigurable Intelligent Surfaces

Philipp Mackensen (Ruhr University Bochum), Paul Staat (Max Planck Institute for Security and Privacy), Stefan Roth (Ruhr University Bochum), Aydin Sezgin (Ruhr University Bochum), Christof Paar (Max Planck Institute for Security and Privacy), Veelasha Moonsamy (Ruhr University Bochum)

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