Adversarial example construction against autonomous vehicles
With autonomous vehicles (AVs) approaching widespread adoption, there is a need to emphasize safety as it must not be neglected. Touted to be free from errors commonly made by humans, they are nevertheless not immune to attacks with malicious intent. In general, AVs utilize a variety of machine-l...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2023
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Online Access: | https://hdl.handle.net/10356/171944 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | With autonomous vehicles (AVs) approaching widespread adoption, there is a need to
emphasize safety as it must not be neglected. Touted to be free from errors commonly
made by humans, they are nevertheless not immune to attacks with malicious intent.
In general, AVs utilize a variety of machine-learning models and sensors to help them
understand their environment. However, based on past research on machine learning
models, it is understood that they may be susceptible to adversarial attacks. In this
paper, Daedalus, an attack algorithm that exploits the vulnerability in Non-Maximum
Suppression (NMS) is used to generate adversarial examples using a surrogate model.
The perturbations on the images are nearly imperceptible. The generated images are
subsequently evaluated against the Single-Stage Monocular 3D Object Detection via
Key Point Estimation [1] (SMOKE) utilized in Baidu Apollo’s Autonomous Driving
System for camera-based object detection. In addition, look into potential mitigations
that could be implemented to mitigate Daedalus. |
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