An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks

11 Mar 2022  ·  Anirudh Yadav, Ashutosh Upadhyay, S. Sharanya ·

According to recent studies, the vulnerability of state-of-the-art Neural Networks to adversarial input samples has increased drastically. A neural network is an intermediate path or technique by which a computer learns to perform tasks using Machine learning algorithms. Machine Learning and Artificial Intelligence model has become a fundamental aspect of life, such as self-driving cars [1], smart home devices, so any vulnerability is a significant concern. The smallest input deviations can fool these extremely literal systems and deceive their users as well as administrator into precarious situations. This article proposes a defense algorithm that utilizes the combination of an auto-encoder [3] and block-switching architecture. Auto-coder is intended to remove any perturbations found in input images whereas the block switching method is used to make it more robust against White-box attacks. The attack is planned using FGSM [9] model, and the subsequent counter-attack by the proposed architecture will take place thereby demonstrating the feasibility and security delivered by the algorithm.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Adversarial Defense miniImageNet Auto Encoder-Block Switching defense with GradCAM Accuracy 88.54 # 1


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