Abstract: A machine learning model is watermarked through fairness bias. To do this, an original set of labeled data is obtained and clustered into a plurality of groups using a clustering algorithm. Labels for data in a subset of the groups are modified, inserting fairness bias into the subset. A machine learning model is trained based on the subset of data labeled using the modified labels and the original set of data outside of the subset labeled using the original set of labels. The machine learning model trained as such exhibits the fairness bias when classifying input data belonging to subset of the plurality of groups. A model exhibiting the fairness bias for input data belonging to the subset is a watermark of a machine learning model that was trained using the modified labels for the subset determined based on the subgroup algorithm. The watermark is usable to determine ownership.

Abstract: Source code is scanned to generate a list of vulnerable tokens. Thereafter, the list of vulnerable tokens is inputted into a machine learning model to identify false positives in the list of vulnerable tokens. Based on this identification, the list of vulnerable tokens can be modified to remove the identified false positives. Related apparatus, systems, techniques and articles are also described.

Abstract: Source code is scanned to generate a list of vulnerable tokens. Thereafter, the list of vulnerable tokens is inputted into a machine learning model to identify false positives in the list of vulnerable tokens. Based on this identification, the list of vulnerable tokens can be modified to remove the identified false positives. Related apparatus, systems, techniques and articles are also described.