• Implemented adversarial attacks using the Fast Gradient Sign Method on CIFAR10 to evaluate the proposed defence mechanism's effectiveness against both white-box attacks • Designed a Defence pipeline involving Denoising Autoencoder and Block Switching Model • Utilized Denoising Autoencoder to filter out perturbations from input images, enhancing the resilience of the model to adversarial noise. Trained it for multiple noise factors ranging from 0.01 to 0.25 for CIFAR10 • Implemented Block-switching using 4 instances of modified VGG16. Re-Trained the combined model with parallel channels and a common upper model; parallel channels can disrupt gradient-based attack vectors. • Successfully defended against strong FGSM attack and classification accuracy increased from 6.84% to 54.36%