Abstract: Robustness of a machine learning model can be characterized by receiving a file with a known, first classification by the machine learning model. Thereafter, a selection is made as to which of a plurality of perturbation algorithms to use to modify the file. The perturbation algorithm is selected as to provide a shortest sequence of actions to cause the machine learning model to provide a desired classification. Subsequently, the received file is iteratively modified using the selected perturbation algorithm and inputting the corresponding modified file into the machine learning model until the machine learning model outputs a known, second classification. Related apparatus, systems, techniques and articles are also described.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.

Abstract: Robustness of a machine learning model can be characterized by receiving a file with a known, first classification by the machine learning model. Thereafter, a selection is made as to which of a plurality of perturbation algorithms to use to modify the file. The perturbation algorithm is selected as to provide a shortest sequence of actions to cause the machine learning model to provide a desired classification. Subsequently, the received file is iteratively modified using the selected perturbation algorithm and inputting the corresponding modified file into the machine learning model until the machine learning model outputs a known, second classification. Related apparatus, systems, techniques and articles are also described.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.