A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

Pytorch code to generate adversarial examples on mnist and ImageNet data.

Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" & "Fixing Data Augmentation to Improve Adversarial Robustness" in PyTorch

Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).

[NeurIPS 2021] Official PyTorch Implementation for "Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck"

a Pytorch library for security research on speaker recognition, released in "Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition" accepted by TDSC

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

A pytorch implementation of "Adversarial Examples in the Physical World"

reproduction of Thermometer Encoding: One Hot Way To Resist Adversarial Examples in pytorch

B.Sc. Final Project: Generating adversarial examples using GAN (Generative Adversarial Network) in Pytorch on the MNIST dataset.

A pytorch implentation of FGSM in paper 'EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES'

Implementation (in pytorch) based Adversarial Examples in the Physical World

Example of the use of Generative Adversarial Networks in Pytorch

A repository containing examples of adversarial machine learning for image classification in PyTorch.

A Python toolbox to create adversarial examples that fool neural networks in PyTorch.

Pytorch project to create adversarial example in a context of predictive maintenance

Fast Gradient Sign Method (FGSM) in PyTorch. Generates single-step perturbations by taking the sign of input gradients to create imperceptible adversarial examples that mislead classifiers. Includes demos and evaluation.