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- W4206996087 abstract "Neural Networks are really impeccable sometimes, but when it comes to adversarial attacks, their performance falls off swiftly. Moreover, considerations to build more robust models that would be resilient to adversarial attacks are ignored frequently. Here, we aim to design a model that is robust and can handle multiple types of adversarial attacks on traffic signs. Despite those attacks, the model should not be fooled and identify the sign correctly. Simple input transformations can help defend against adversarial attacks. We used the German Traffic Sign Recognition Benchmark (GTSRB) dataset to generate adversarial samples by Fast Gradient Sign Method [1] and Projected Gradient Descent Method [1]. In this paper, we focus on training a model with a defense mechanism to remove the adversarial noise, to make the model Anti-Spoof for Traffic Sign Recognition." @default.
- W4206996087 created "2022-01-26" @default.
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- W4206996087 date "2022-01-01" @default.
- W4206996087 modified "2023-09-29" @default.
- W4206996087 title "AI Approach for Autonomous Vehicles to Defend from Adversarial Attacks" @default.
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- W4206996087 doi "https://doi.org/10.1007/978-981-16-7136-4_17" @default.
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