Automatically quantifying a robustness of an object detection model applied for a controlling task and/or a monitoring task

A computer-implemented method for automatically quantifying a robustness of an object detection model applied for a controlling task and/or a monitoring task, comprising:

• receiving the object detection model which is trained to output a predicted object in terms of a location in an image data and of an object class out of a set of object classes when the image data is input into the object detection model,
• applying a set of robustness requirements to the object detection model,
• deriving from each robustness requirement a cross Lipschitz-ness functions, which quantifies the robustness requirement conditioned on the object detection model for the input image data,
• determining a robustness value of the object detection model by calculating the cross Lipschitz-ness function integrated into a Cross Lipschitz Extreme Value for Network Robustness CLEVER score of the object detection model for a perturbed image data deviating from the un-perturbed image data of the image data,
• comparing the determined robustness value with a predefined robustness threshold value, and
• outputting a positive certification for applying the object detection model in the controlling task and/or the monitoring task if the robustness value is below the predefined robustness threshold value, wherein each robustness requirement is defined by stating a scenario where the object detection model is not considered to be robust.