advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

Abstract

advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch is built on PyTorch (Paszke et al., 2017), and leverages the advantages of the dynamic computational graph to provide concise and efficient reference implementations. The code is licensed under the LGPL license and is open sourced at https://github.com/BorealisAI/advertorch .

References in corpus (7)

• Explaining and Harnessing Adversarial Examples
• Countering Adversarial Images using Input Transformations
• A study of the effect of JPG compression on adversarial images
• Spatially Transformed Adversarial Examples
• Simple Black-Box Adversarial Perturbations for Deep Networks
• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
• Classification regions of deep neural networks

Cited by in corpus (4)

• Batch Normalization is a Cause of Adversarial Vulnerability
• Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
• Adversarial Attack on Deep Product Quantization Network for Image Retrieval
• Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks