首个原生支持多卡的对抗机器学习工具包，兼顾灵活配置与高可复现性。
AdvSecureNet: A Python Toolkit for Adversarial Machine Learning
- 基于PyTorch，原生支持多GPU并行，加速攻击、防御与评估流程。
- 同时提供命令行与API接口，支持外部YAML配置，提升实验可复现性。
- 集成多种攻防方法与评估指标，适合安全研究者快速搭建实验环境。
机器学习模型易受对抗攻击影响。尽管已有若干工具用于研究此类漏洞，但普遍功能不全且灵活性不足。我们提出AdvSecureNet，一个基于PyTorch的对抗机器学习工具包，是首个原生支持多GPU的攻击、防御与评估框架。该工具包首次同时支持命令行接口（CLI）与应用接口（API），并通过外部YAML配置文件增强灵活性与实验可复现性。工具包内置多种攻击算法、防御策略及评估指标。项目遵循严格的软件工程实践，确保代码质量与可维护性。项目已开源，可通过GitHub（https://github.com/melihcatal/advsecurenet）获取，亦可通过PyPI安装。
原文摘要 · Abstract (English)
Machine learning models are vulnerable to adversarial attacks. Several tools have been developed to research these vulnerabilities, but they often lack comprehensive features and flexibility. We introduce AdvSecureNet, a PyTorch based toolkit for adversarial machine learning that is the first to natively support multi-GPU setups for attacks, defenses, and evaluation. It is the first toolkit that supports both CLI and API interfaces and external YAML configuration files to enhance versatility and reproducibility. The toolkit includes multiple attacks, defenses and evaluation metrics. Rigiorous software engineering practices are followed to ensure high code quality and maintainability. The project is available as an open-source project on GitHub at https://github.com/melihcatal/advsecurenet and installable via PyPI.
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