An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks
This addresses security concerns for AI systems like self-driving cars and smart devices, but it appears incremental as it builds on existing methods like auto-encoders and block-switching.
The paper tackles the vulnerability of neural networks to adversarial attacks by proposing a defense algorithm that combines an auto-encoder and block-switching architecture, resulting in demonstrated feasibility and security against white-box attacks using the FGSM model.
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.