Interpreting Adversarial Examples and Robustness for Deep Learning-Based Auto-Driving Systems

doi:10.1109/TITS.2021.3108520

Interpreting Adversarial Examples and Robustness for Deep Learning-Based Auto-Driving Systems

2022-07-01

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AI

Mohammad Mehedi Hassan *

DOI：10.1109/TITS.2021.3108520

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Deep learning-based auto-driving systems are vulnerable to adversarial examples attacks which may result in wrong decision making and accidents. An adversarial example can fool the well trained neural networks by adding barely imperceptible perturbations to clean data. In this paper, we explore the mechanism of adversarial examples and adversarial robustness from the perspective of statistical mechanics, and propose an statistical mechanics-based interpretation model of adversarial robustness. The state transition caused by adversarial training based on the theory of fluctuation dissipation disequilibrium in statistical mechanics is formally constructed. Besides, we fully study the adversarial example attacks and training process on system robustness, including the influence of different training processes on network robustness. Our work is helpful to understand and explain the adversarial examples problems and improve the robustness of deep learning-based auto-driving systems.

Keywords:

Auto-driving systems

adversarial examples

statistic mechanic

adversarial training

adversarial robustness

Interpreting Adversarial Examples and Robustness for Deep Learning-Based Auto-Driving Systems

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