The choice of an SVM kernel corresponds to the choice of a rep(cid:173) resentation of the data in a feature space and, to improve per(cid:173) formance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in non(cid:173) linear kernels. We show on a digit recognition task that the pro(cid:173) posed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.

Next week at AI Research Week, hosted by the MIT-IBM Watson AI Lab in Cambridge, MA, we will publish the first major release of the Adversarial Robustness 360 Toolbox (ART). Initially released in April 2018, ART is an open-source library for adversarial machine learning that provides researchers and developers with state-of-the-art tools to defend and verify AI models against adversarial attacks. ART v1.0 marks a milestone in AI security, introducing new features that extend ART to conventional machine learning models and a variety of data types beyond images: The number of reports on real-world exploitations using adversarial attacks against AI is growing, as in the case of anti-virus software, highlighting the importance of understanding, improving and monitoring the adversarial robustness of AI models. ART provides a comprehensive and growing set of tools to systematically assess and improve the robustness of AI models against adversarial attacks, including evasion and poisoning. In evasion attacks, the adversary crafts small changes to the original input to an AI model in order to influence its behaviour.