Ashish Hooda (University of Wisconsin-Madison), Andrey Labunets (UC San Diego), Tadayoshi Kohno (University of Washington), Earlence Fernandes (UC San Diego)

Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashing algorithms help determine whether two images are visually similar while preserving the privacy of the input images. Several efforts from industry and academia propose scanning on client devices such as smartphones due to the impending rollout of end-to-end encryption that will make server-side scanning difficult. These proposals have met with strong criticism because of the potential for the technology to be misused for censorship. However, the risks of this technology in the context of surveillance are not well understood. Our work informs this conversation by experimentally characterizing the potential for one type of misuse --- attackers manipulating the content scanning system to perform physical surveillance on target locations. Our contributions are threefold: (1) we offer a definition of physical surveillance in the context of client-side image scanning systems; (2) we experimentally characterize this risk and create a surveillance algorithm that achieves physical surveillance rates of more than 30% by poisoning 0.2% of the perceptual hash database; (3) we experimentally study the trade-off between the robustness of client-side image scanning systems and surveillance, showing that more robust detection of illegal material leads to an increased potential for physical surveillance in most settings.

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Eavesdropping on Black-box Mobile Devices via Audio Amplifier's EMR

Huiling Chen (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Wenqiang Jin (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Yupeng Hu (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Zhenyu Ning (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Kenli Li (College…

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Work-in-Progress: Manifest V3 Unveiled: Navigating the New Era of...

Nikolaos Pantelaios and Alexandros Kapravelos (North Carolina State University)

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CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving...

Sampath Rajapaksha, Harsha Kalutarage (Robert Gordon University, UK), Garikayi Madzudzo (Horiba Mira Ltd, UK), Andrei Petrovski (Robert Gordon University, UK), M.Omar Al-Kadri (University of Doha for Science and Technology)

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SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

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