论文标题
Caisar：一个表征人工智能安全性和鲁棒性的平台
CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness
论文作者
论文摘要
我们提出了Caisar，这是一个正在积极开发的开源平台，以表征AI Systems的鲁棒性和安全性。 Caisar提供了一个统一的入口点，用于使用WhyMl（Why3验证平台的成熟和表达语言）来定义验证问题。此外，凯萨尔（Caisar）编排和创建了最先进的机器学习验证工具，该工具单独无法有效地处理所有问题，但总体上可以涵盖越来越多的属性。我们的目的是一方面，通过减少选择针对给定验证问题量身定制的方法的负担来协助V \＆V流程，另一方面，通过将有用的功能 - 可视化，报告生成，属性描述中的一个平台分配给工具开发人员。 CAISAR很快将在https://git.frama-c.com/pub/caisar提供。
We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive language of the Why3 verification platform. Moreover, CAISAR orchestrates and composes state-of-the-art machine learning verification tools which, individually, are not able to efficiently handle all problems but, collectively, can cover a growing number of properties. Our aim is to assist, on the one hand, the V\&V process by reducing the burden of choosing the methodology tailored to a given verification problem, and on the other hand the tools developers by factorizing useful features-visualization, report generation, property description-in one platform. CAISAR will soon be available at https://git.frama-c.com/pub/caisar.
