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Comprehensive analysis on laser spots adversarial attacks using genetic algorithm
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DOI：10.1016/j.iswa.2025.200598.png)
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Abstract
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Deep Neural Networks (DNNs) are highly vulnerable to disruptions caused by minimal noise, yet research on physical attacks leveraging light-based methods remains scarce. Light-based physical attacks are exceptionally stealthy, posing substantial security threats to vision-dependent applications such as autonomous driving. This paper enhances a state-of-the-art light-based physical attack that employs a genetic algorithm to optimize laser spot placement for maximum effectiveness. We expand the algorithm by introducing additional hyperparameters and systematically optimizing them to establish the most efficient workflow for this problem. To our knowledge, this is the first light-based attack capable of reliably performing physical attacks during daylight conditions, making it the most effective and robust approach of its kind. Extensive experiments conducted in a digital environment demonstrate the superiority of the genetic algorithm over random-location methods. By identifying optimal hyperparameter values, we achieve significant improvements in both performance and efficiency. Specifically, we managed to achieve an Attack Success Rate (ASR) of 89.7%, with an Average Query (AQ) of only 109.4, demonstrating a highly efficient and effective approach. The results reveal that laser spots can severely interfere with advanced DNNs, highlighting the critical security risks associated with this technique.
Keywords:
Physical attacks
Light-based attacks
Adverse perturbations
Hyperparameters optimization
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4.3
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90
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