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This repository contains the implementation of two adversarial example attack methods FGSM, IFGSM and one Input Transformation defense mechanism against all attacks using Imagenet dataset.
This repository is meant to train and test LeNet and MLP CNN Models on adversial attack like fgsm and then usage of defense algorithm to minimize effect of attack.
This project tests multiple different machine learning algorithms that can detect adversarial attacks in multi-agent reinforcement learning settings. Baselines were used to compare performance of a proposed ensemble model. Then, using FGSM, we re-attacked the ensemble detection model with perturbed observations. Read more at the pdf titled Final…
ECE C147: Neural Networks & Deep Learning. Repository for "Developing Robust Networks to Defend Against Adversarial Examples". Implementing adversarial data augmentation on CNNs and RNNs.