Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

5 Dec 2017Kexin PeiYinzhi CaoJunfeng YangSuman Jana

Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties... (read more)

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