no code implementations • 7 Jan 2024 • Takami Sato, Sri Hrushikesh Varma Bhupathiraju, Michael Clifford, Takeshi Sugawara, Qi Alfred Chen, Sara Rampazzi
We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras.
no code implementations • 30 Aug 2023 • Takami Sato, Justin Yue, Nanze Chen, Ningfei Wang, Qi Alfred Chen
The NDD attack shows a significantly high capability to generate low-cost, model-agnostic, and transferable adversarial attacks by exploiting the natural attack capability in diffusion models.
no code implementations • ICCV 2023 • Ningfei Wang, Yunpeng Luo, Takami Sato, Kaidi Xu, Qi Alfred Chen
In this work, we conduct the first measurement study on whether and how effectively the existing designs can lead to system-level effects, especially for the STOP sign-evasion attacks due to their popularity and severity.
no code implementations • 19 Mar 2023 • Takami Sato, Yuki Hayakawa, Ryo Suzuki, Yohsuke Shiiki, Kentaro Yoshioka, Qi Alfred Chen
To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets.
no code implementations • 9 Mar 2023 • Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu
We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.
1 code implementation • ICCV 2023 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In this paper, we present a novel adversarial training method for trajectory prediction.
no code implementations • 27 May 2022 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.
1 code implementation • CVPR 2022 • Takami Sato, Qi Alfred Chen
After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods.
no code implementations • 10 Mar 2022 • Junjie Shen, Ningfei Wang, Ziwen Wan, Yunpeng Luo, Takami Sato, Zhisheng Hu, Xinyang Zhang, Shengjian Guo, Zhenyu Zhong, Kang Li, Ziming Zhao, Chunming Qiao, Qi Alfred Chen
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
no code implementations • 6 Jul 2021 • Takami Sato, Qi Alfred Chen
We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios.
no code implementations • 27 Feb 2021 • Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu
The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.
no code implementations • 14 Sep 2020 • Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.
no code implementations • 3 Mar 2020 • Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.