Abstract: Systems and/or techniques for facilitating image forgery detection via pixel-metadata consistency analysis are provided. In various embodiments, a system can receive an electronic image from a client device. In various cases, the system can obtain a pixel vector and/or an image metadata vector that correspond to the electronic image. In various aspects, the system can determine whether the electronic image is authentic or forged, based on analyzing the pixel vector and the image metadata vector via at least one machine learning model.

Abstract: Systems and/or techniques for facilitating image forgery detection via headpose estimation are provided. In various embodiments, a system can receive a document from a client device. In various cases, the system can identify, by executing a first trained machine learning model, an object that is depicted in the document. In various instances, the system can determine, by executing a second trained machine learning model, a pose of the object. In various aspects, the system can determine, by executing a third trained machine learning model, whether the document is authentic or forged based on the pose of the object. In various embodiments, the system can, in response to determining that the document is forged, transmit an unsuccessful validation message to the client device.