An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

Han Qiu

doi:10.1109/TC.2021.3076826

An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

2024-03-01

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DOI：10.1109/TC.2021.3076826

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Deep Neural Networks are well-known to be vulnerable to Adversarial Examples. Recently, advanced gradient-based attacks were proposed (e.g., BPDA and EOT), which can significantly increase the difficulty and complexity of designing effective defenses. In this paper, we present a study towards the opportunity of mitigating those powerful attacks with only pre-processing operations. We make the following two contributions. First, we perform an in-depth analysis of those attacks and summarize three fundamental properties that a good defense solution should have. Second, we design a lightweight preprocessing function with these properties and the capability of preserving the model's usability and robustness against these threats. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Keywords:

Perturbation methods

Training

Computational modeling

Robustness

Predictive models

Neural networks

Mathematical model

Adversarial examples

deep learning

adversarial attacks

BPDA

An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

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