A framework for robust deep learning models against adversarial attacks based on a protection layer approach

Tan, Shing Chiang and Mohammed Al-Andoli, Mohammed Nasser and Goh, Pey Yun and Sim, Kok Swee and Lim, Chee Peng (2024) A framework for robust deep learning models against adversarial attacks based on a protection layer approach. IEEE Access, 12. pp. 17522-17540. ISSN 2169-3536

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Abstract

Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs). These AEs are meticulously crafted by adversaries, introducing imperceptible perturbations to clean data to deceive DL models. Consequently, AEs pose potential risks to DL applications. In this paper, we propose an effective framework for enhancing the robustness of DL models against adversarial attacks. The framework leverages convolutional neural networks (CNNs) for feature learning, Deep Neural Networks (DNNs) with softmax for classification, and a defense mechanism to identify and exclude AEs. Evasion attacks are employed to create AEs to evade and mislead the classifier by generating malicious samples during the test phase of DL models i.e., CNN and DNN, using the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Square Attack (SA). A protection layer is developed as a detection mechanism placed before the DNN classifier to identify and exclude AEs. The detection mechanism incorporates a machine learning model, which includes one of the following: Fuzzy ARTMAP, Random Forest, K-Nearest Neighbors, XGBoost, or Gradient Boosting Machine. Extensive evaluations are conducted on the MNIST, CIFAR-10, SVHN, and Fashion-MNIST data sets to assess the effectiveness of the proposed framework. The experimental results indicate the framework's ability to effectively and accurately detect AEs generated by four popular attacking methods, highlighting the potential of our developed framework in enhancing its robustness against AEs.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Adversarial examples, Security, Adversarial attacks, Adversarial examples detection
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	01 Jul 2024 14:28
Last Modified:	01 Jul 2024 14:28
URI:	http://eprints.utem.edu.my/id/eprint/27255
Statistic Details:	View Download Statistic

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