Go Tsuruoka (Waseda University), Takami Sato, Qi Alfred Chen (University of California, Irvine), Kazuki Nomoto, Ryunosuke Kobayashi, Yuna Tanaka (Waseda University), Tatsuya Mori (Waseda University/NICT/RIKEN)

Traffic signs, essential for communicating critical rules to ensure safe and efficient traffic for entities such as pedestrians and motor vehicles, must be reliably recognized, especially in the realm of autonomous driving. However, recent studies have revealed vulnerabilities in vision-based traffic sign recognition systems to adversarial attacks, typically involving small stickers or laser projections. Our work advances this frontier by exploring a novel attack vector, the Adversarial Retroreflective Patch (ARP) attack. This method is stealthy and particularly effective at night by exploiting the optical properties of retroreflective materials, which reflect light back to its source. By applying retroreflective patches to traffic signs, the reflected light from the vehicle’s headlights interferes with the camera, causing perturbations that hinder the traffic sign recognition model’s ability to correctly detect the signs. In our preliminary study, we conducted a feasibility study of ARP attacks and observed that while a 100% attack success rate is achievable in digital simulations, it decreases to less than or equal to 90% in physical experiments. Finally, we discuss the current challenges and outline our future plans. This research gains significance in the context of autonomous vehicles’ 24/7 operation, emphasizing the critical need to assess sensor and AI vulnerabilities, especially in low-light nighttime environments, to ensure the continued safety and reliability of self-driving technologies.

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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From Interaction to Independence: zkSNARKs for Transparent and Non-Interactive...

Shahriar Ebrahimi (IDEAS-NCBR), Parisa Hassanizadeh (IDEAS-NCBR)

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Enhance Stealthiness and Transferability of Adversarial Attacks with Class...

Hui Xia (Ocean University of China), Rui Zhang (Ocean University of China), Zi Kang (Ocean University of China), Shuliang Jiang (Ocean University of China), Shuo Xu (Ocean University of China)

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