期刊
IEEE SIGNAL PROCESSING MAGAZINE
卷 38, 期 1, 页码 42-52出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MSP.2020.2983666
关键词
Image segmentation; Perturbation methods; Semantics; Cameras; Sensors; Task analysis; Autonomous vehicles
资金
- Volkswagen Group Automation, Wolfsburg, Germany
Enabling autonomous driving through environment perception using CNNs for semantic segmentation is a complex task with vulnerabilities to adversarial attacks. Understanding the advantages and disadvantages of CNNs in this context is crucial for future research in the field.
Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed stateof-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
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