  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.
