Style classification for authentic content search

Abstract

Various aspects of the subject technology relate to systems, methods, and machine-readable media for authentic content search using style classifications. A system may be a search engine that uses a set of style classifiers to detect one or more styles associated with an image and a logistic regression model to determine a level of authenticity for the image based on the associated styles. Training images are fed to train a series of neural networks that output a set of style classifiers. An image is processed through the style classifiers to determine respective probabilities for each style classification. The results from the set of style classifiers are then input to the logistic regression model to determine an authenticity score for the image. For example, the authenticity score shows how authentic is an image (e.g., a score of 1.0 refers to 100% authenticity, whereas a score of 0.0 represents a non-authentic image).