gralliry/Adversarial-Attack-Generation-Techniques
Adversarial attack generation techniques for CIFAR10 based on Pytorch: L-BFGS, FGSM, I-FGSM, MI-FGSM, DeepFool, C&W, JSMA, ONE-PIXEL, UPSET
This project helps machine learning engineers and researchers evaluate the robustness of image classification models. It allows you to generate various types of 'adversarial attacks' on images, which are subtle modifications designed to fool a model. You input a trained image classification model and a dataset of images, and it outputs attack examples and the model's accuracy after being attacked.
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Use this if you need to understand how vulnerable your image recognition models are to malicious inputs and compare different attack strategies.
Not ideal if you are looking to build a robust defense mechanism against adversarial attacks, as this project focuses on attack generation.
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Language
Python
License
MIT
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Last pushed
Sep 13, 2025
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