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

arXiv: https://arxiv.org/abs/1902.07623 · HTML: https://mlsanity.com/paper/1902.07623/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 30 of 69)

- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints](https://mlsanity.com/paper/2102.12827/) (2021)
- [Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?](https://mlsanity.com/paper/2203.08392/) (2022)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [PAC Prediction Sets Under Covariate Shift](https://mlsanity.com/paper/2106.09848/) (2021)
- [Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment](https://mlsanity.com/paper/2505.21494/) (2025)
- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks](https://mlsanity.com/paper/2110.14068/) (2021)
- [Understanding the Dynamics of DNNs Using Graph Modularity](https://mlsanity.com/paper/2111.12485/) (2021)
- [Towards Defending against Adversarial Examples via Attack-Invariant Features](https://mlsanity.com/paper/2106.05036/) (2021)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks](https://mlsanity.com/paper/2407.10179/) (2024)
- [Removing Adversarial Noise in Class Activation Feature Space](https://mlsanity.com/paper/2104.09197/) (2021)
- [On the Effectiveness of Adversarial Training against Backdoor Attacks](https://mlsanity.com/paper/2202.10627/) (2022)
- [TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation](https://mlsanity.com/paper/2103.10274/) (2021)
- [Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?](https://mlsanity.com/paper/2112.01601/) (2021)
- [Dormant: Defending against Pose-driven Human Image Animation](https://mlsanity.com/paper/2409.14424/) (2024)
- [DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks](https://mlsanity.com/paper/2110.07305/) (2021)
- [Detecting AutoAttack Perturbations in the Frequency Domain](https://mlsanity.com/paper/2111.08785/) (2021)
- [Discriminator-Free Generative Adversarial Attack](https://mlsanity.com/paper/2107.09225/) (2021)
- [Exploring Adversarially Robust Training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2202.09300/) (2022)
- [Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions](https://mlsanity.com/paper/2104.11103/) (2021)
- [Instance Correction for Learning with Open-set Noisy Labels](https://mlsanity.com/paper/2106.00455/) (2021)
- [Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models](https://mlsanity.com/paper/2110.08712/) (2021)
- [Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks](https://mlsanity.com/paper/2111.01714/) (2021)
- [Graph-Based Similarity of Neural Network Representations](https://mlsanity.com/paper/2111.11165/) (2021)
- [Adversarial Learning with Cost-Sensitive Classes](https://mlsanity.com/paper/2101.12372/) (2021)
- [Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks](https://mlsanity.com/paper/2204.12050/) (2022)
- [Towards Out-of-Distribution Adversarial Robustness](https://mlsanity.com/paper/2210.03150/) (2022)
- [Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks](https://mlsanity.com/paper/2404.03340/) (2024)

## Abstract (arXiv)

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 .