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Implementation of Papers on Adversarial Examples
A defense algorithm which utilizes the combination of an auto- encoder and block-switching architecture. Auto-coder is intended to remove any perturbations found in input images whereas block switching method is used to make it more robust against White-box attack. Attack is planned using FGSM model, and the subsequent counter-attack by the proposed architecture will take place thereby demonstrating the feasibility and security delivered by the algorithm.
Official implementation of the paper DiffDefence: defending against adversarial attacks via diffusion models. ICIAP 2023.
Proposed defenses against several adversarial attacks for speech to text systems
Evaluation of various defence mechanisms and various UAPs. Done as a part of GD-UAP.
This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"