DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks. As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. Adversary can mislead GNNs to give wrong predictions by modifying the graph structure such as manipulating a few edges. This vulnerability has arisen tremendous concerns for adapting GNNs in safety-critical applications and has attracted increasing research attentio…

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

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 .

Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in making predictions for downstream tasks. The vulnerability to adversarial attacks has raised increasing concerns for applying GNNs in safety-critical applications. Therefore, developing robust algorithms to defend adversarial attacks is of great significance. A natural idea to defend adversarial attacks is to clean the perturbed graph. It is evident…

Deep Defense: Training DNNs with Improved Adversarial Robustness

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models lear…

AdvSecureNet：一个用于对抗机器学习的 Python 工具包

AdvSecureNet: A Python Toolkit for Adversarial Machine Learning

本文提出基于 PyTorch 的对抗机器学习工具包 AdvSecureNet，首次原生支持多 GPU 攻击、防御与评估，并同时提供 CLI、API 和 YAML 配置接口，以提升灵活性和可复现性。

统一梯度以提升深度网络的真实世界鲁棒性

Unifying Gradients to Improve Real-world Robustness for Deep Networks

针对深度神经网络面临的基于分数的黑盒查询攻击威胁，提出即插即用的统一梯度（UniG）防御方法，通过哈达玛积模块统一不同数据梯度以误导攻击方向，在多个数据集和攻击场景下显著提升鲁棒性且保持高干净准确率。