Cascade ensembles for liveness detection

A system for liveness detection, the system comprising:

• an image capturing device for capturing an image or video for performing liveness detection thereon;
• computing processor hardware;
• software executable on the computing processor hardware that implements:
• a threat detector configured to receive the image or the video, and one or more filters configured to perform a first level check for detecting a threat based on analysis of one or more characteristics on the image or the video received from the threat detector without detecting a classification or an identification of the threat, wherein on detecting the threat by the one or more filters, the threat detector registers a final threat verdict of the whole system;
• one or more special models configured to perform a second level check on the image or the video, wherein the one or more special models are configured for detecting multiple types of threats on the image or the video, wherein each special model is trained to detect a predefined type of a threat, wherein the verdicts of all special models is passed to an aggregator model, wherein on detecting the threat by the aggregator, the threat detector registers the final threat verdict of the whole system;
• a general model ensemble configured to perform a third level check on the image or the video, wherein the general model ensemble includes multiple general threat detection models configured to classify the image or the video according to individual features into original and fake, wherein the general model ensemble is trained to classify the image or video as live or not based on a verdict of each general threat detection model verdict, wherein on detecting the threat by the general model ensemble, the threat detector registers the final verdict of the whole system.

A system for threat detection of an image, the system comprising:

• computing processor hardware;
• software executable on the computing processor hardware that implements a plurality of filters configured to determine a first verdict of the image when at least one of the plurality of filters detects an attack based on a probability specific to each of the plurality of filters;
• a plurality of special models configured to, when the first verdict is not associated with an attack, determine a second verdict of the image when at least one of the special models detects an attack, wherein each of the plurality of special models is trained to detect a different type of threat;
• a general model ensemble including a plurality of threat detection models arranged in an ensemble, and configured to, when the second verdict is not associated with an attack, determine a probability of attack specific to each of the plurality of threat detection models;
• and
• an aggregator configured to aggregate each of the probabilities of attack specific to each of the plurality of threat detection models to determine a third verdict.