Abstract
Owing to the recent remarkable advancements in computer vision and artificial intelligence, resilient self-driving cars are now becoming a reality. In this paper, we detail an autonomous car prototype that uses vision-based perception for control and navigation. The car uses a deep neural network to classify traffic symbols based on which motor control decisions are taken. The classification model is tested in various environmental settings and is observed to be robust. In real-world situations, misclassification in autonomous systems can lead to serious accidents. Although the classification model is reliable, there may arise different environmental scenarios where misclassification can occur. In order to simulate such a scenario, we explore the effects of different adversarial attacks on the network. It is observed that the attacks are able to trick the network into misclassifying symbols. Therefore, a network defense mechanism is crucial for handling such scenarios. We present a structure-based learning approach for a robust defense mechanism. The approach is self-supervised and it leverages the use of data augmentations of a sample to have different representations of the same. This ensures that the network is trained to learn the base structure for various environmental settings, thereby nullifying the effect of different adversarial attacks.
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Sharma, M.K., Choudhuri, R., Dixit, M., Sarkar, M., Dittakavi, B. (2023). Structure-Based Learning for Robust Defense Against Adversarial Attacks in Autonomous Driving Agents. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_36
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DOI: https://doi.org/10.1007/978-3-031-31417-9_36
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