# Advbox: a toolbox to generate adversarial examples that fool neural networks

arXiv: https://arxiv.org/abs/2001.05574 · HTML: https://mlsanity.com/paper/2001.05574/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 17 of 17)

- [BackdoorBench: A Comprehensive Benchmark of Backdoor Learning](https://mlsanity.com/paper/2206.12654/) (2022)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [KoDF: A Large-scale Korean DeepFake Detection Dataset](https://mlsanity.com/paper/2103.10094/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning](https://mlsanity.com/paper/2407.19845/) (2024)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses](https://mlsanity.com/paper/2308.03258/) (2023)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [SEC4SR: A Security Analysis Platform for Speaker Recognition](https://mlsanity.com/paper/2109.01766/) (2021)
- [URET: Universal Robustness Evaluation Toolkit (for Evasion)](https://mlsanity.com/paper/2308.01840/) (2023)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)
- [PRBench: A Standardized Probabilistic Robustness Benchmark](https://mlsanity.com/paper/2511.01724/) (2025)
- [AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient](https://mlsanity.com/paper/2210.06888/) (2022)
- [Generalized Attacks on Face Verification Systems](https://mlsanity.com/paper/2309.05879/) (2023)
- [AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning](https://mlsanity.com/paper/2312.17667/) (2023)
- [BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning](https://mlsanity.com/paper/2401.15002/) (2024)

## Abstract (arXiv)

In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful neural networks. Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, TensorFlow and it can benchmark the robustness of machine learning models. Compared to previous work, our platform supports black box attacks on Machine-Learning-as-a-service, as well as more attack scenarios, such as Face Recognition Attack, Stealth T-shirt, and DeepFake Face Detect. The code is licensed under the Apache 2.0 and is openly available at https://github.com/advboxes/AdvBox. Advbox now supports Python 3.