主讲人：郑银强

主讲人简介：郑银强教授于2013年在日本东京东京工业大学机械与控制工程系获得工程博士学位。他目前是日本东京大学下一代人工智能研究中心的正教授，领导光学传感和相机系统实验室（OSCARS实验室）。他发表了一系列关于光学成像和机器学习的研究论文。在与佳能和日立的合作中，他为多波段光声成像系统和显微荧光成像系统的开发和商业化做出了重大贡献。他曾担任 CVPR、ICCV、MM、3DV、ACCV、ISAIR、DICTA 和 MVA 等CCF A类会议的区域主席。他曾获得久负盛名的船井学术奖和柯尼卡美能达成像科学激励奖。

讲座摘要: AI algorithms for computer-based visual understanding have advanced significantly, due to the prevalence of deep learning and large-scale visual datasets in the RGB domain, which have also been proven vulnerable to digital and physical adversarial attacks. To deal with complex scenarios, many other imaging modalities beyond the visibility scope of human eyes, such as near infrared (NIR), thermal infrared (TIR), polarization, have been introduced, yet the vulnerabilities of visual AI based on these non-RGB modalities have not received due attention. In this talk, we will show that typical AI algorithms, like object detection and segmentation, can be more fragile than in the RGB domain. We showcase two physical attackers onto the YOLO-based human detector in the NIR and TIR domain, and one projection-based attacker onto the glass segmentation algorithm in the polarization-color domain, all of which are sufficiently concealing to human eyes.

 时间：2024年5月24日下午15:00-18:00

地点：1号学院楼204