Abstract: Described are methods and systems for training a machine learning (ML) model to detect anomalies in images of documents. A first image of a first set of images of documents is obtained. Each first image relates to a region of the document and the first set of images comprises an image of a document containing an anomaly and an image of a document not containing an anomaly. Signal processing algorithms are applied to the first images to generate a signal for each first image and each algorithm, and a discriminative power of each algorithm is evaluated. Based on the discriminative power, a signal processing algorithm is selected and ML model input data is generated using signals generated by applying the algorithm to second digital images. The ML model is trained using the input data to produce output indicating whether an image of a document contains an anomaly.

Abstract: Described herein are computerized methods and systems for detecting fraud during identity verification. An image capture device of a mobile device captures video comprising a plurality of frames of a person's face and transmits the plurality of frames to a server device. The server detects locations of rigid and non-rigid facial features of the person's face in each of the plurality of frames. The server generates time-series signals based upon a position measurement for the facial features in each of the plurality of frames and extracts classification features from the time-series signals. The server applies a trained machine learning classification model to the extracted classification features to generate a fraud detection decision for the plurality of frames.

Abstract: Described are methods and systems for training a machine learning (ML) model to detect anomalies in images of documents. A first image of a first set of images of documents is obtained. Each first image relates to a region of the document and the first set of images comprises an image of a document containing an anomaly and an image of a document not containing an anomaly. Signal processing algorithms are applied to the first images to generate a signal for each first image and each algorithm, and a discriminative power of each algorithm is evaluated. Based on the discriminative power, a signal processing algorithm is selected and ML model input data is generated using signals generated by applying the algorithm to second digital images. The ML model is trained using the input data to produce output indicating whether an image of a document contains an anomaly.