Abstract: Certain aspects and features of this disclosure relate to chromatic undertone detection. For example, a method involves receiving an image file and producing, using a color warmth classifier, an image warmth profile from the image file. The method further involves applying a surface-image-trained machine-learning model to the image warmth profile to produce an inferred undertone value for the image file. The method further involves comparing, using a recommendation module, and the inferred undertone value, an image color value to a plurality of pre-existing color values corresponding to a database of production images, and causing, in response to the comparing, interactive content including the at least one production image selection from the database of production images to be provided on a recipient device.

Abstract: Systems and methods mitigate aggregate exposure of identifying information using machine learning. A privacy monitoring system identifies entities and corresponding entity types by applying a set of domain-specific neural networks, each trained to recognize a particular entity type, to media data extracted from two or more content items associated with a user. The privacy monitoring system computes a privacy score indicating a cumulative privacy risk for potential exposure of identifying information associated with the user from the two or more content items by identifying connections between the identified entities. The connections between the entities are weighted according to the entity types and contribute to the privacy score. A reporting subsystem outputs an indication of a recommended action for mitigating the cumulative privacy risk.