A method for determining authenticity of a document in real-time is disclosed. The method being performed by a processor includes receiving image data of a document. The image data corresponds to at least two images of the document taken simultaneously using at least two cameras. The method includes analyzing the image data to determine a plurality of measurements corresponding to the document along three dimensions. The method includes a thickness at a plurality of location points on the document based on the plurality of measurements, and determining authenticity of the document in real-time based on the determined thickness of the document at the plurality of location points.

The disclosure relates to systems, methods and computer readable media for providing network-based identification, generation and management of a unique damage (finger) print of vehicle(s) by geodetic mapping of stable key points onto a ground truth 3D model of the vehicle, and vehicle parts—identified from the raw images using supervised and unsupervised machine learning. Specifically, the disclosure relates to System and methods for the generation of unique damage print on a vehicle that is obtained from captured images of the damaged vehicle, photogrammetrically localized to a specific vehicle part, and the computer programs enabling the method, the damage print configured to be used, for example, in fraud detection in insurance claims.

Aspects described herein may provide detection of a physical characteristic of a credential, thereby allowing for authentication of the credential. According to some aspects, these and other benefits may be achieved by detecting the physical characteristic with the credential. An image of a credential may be received. An optical characteristic of a secure feature of the credential may be determined. An expected optical characteristic of the secure feature may be determined based on known properties of the secure feature. A determination as to whether the credential is authentic may be based on a comparison of the determined optical characteristic of the secure feature to the expected optical characteristic of the secure feature.

Systems for profile management and control are provided. A system may receive an instrument or image of an instrument. In some examples, data may be extracted from the instrument or image of the instrument and a document profile may be retrieved based on the extracted data. Images within the document profile may be evaluated to identify a type of document for each document. In some examples, a total number of documents of each type may be determined or identified. The total number of documents may be compared to a threshold. If the total number of documents is below the threshold, the documents or images in the profile may be maintained. If the total number of documents is at or above the threshold, in some examples, each document may be further evaluated to determine or identify documents or document images for deletion. In some arrangements, the profile may be refreshed and documents or images identified for deletion may be deleted.

A hologram detection method according to an aspect of the disclosure, includes: inputting a first image, obtained by capturing a detection object on the basis of first flash intensity, to a neural network model to obtain a first detection result value representing the detection or not of a hologram for each of predetermined at least one detection unit regions; and comparing a threshold value with the first detection result value obtained for each detection unit region to determine the detection or not of a hologram in the first image and a first detection unit region where a hologram is detected.

Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

A system, apparatus, method and computer program product are provided for determining a mobile device integrity status. Images of a mobile device captured by the mobile device and using a reflective surface are processed with various trained models, such as neural networks, to verify authenticity, detect damage, and to detect occlusions. A mask may be generated to enable identification of concave occlusions or blocked corners of an object, such as a mobile device, in an image. Images of the front and/or rear of a mobile device may be processed to determine the mobile device integrity status such as verified, not verified, or inconclusive. A user may be prompted to remove covers, remove occlusions, and/or move the mobile device closer to the reflective surface. A real-time response relating to the mobile device integrity status may be provided. The trained models may be trained to improve the accuracy of the mobile device integrity status.

A system and methods are provided for determining a vehicle action during a phantom projection attack, including processing a received image to identify a traffic object, and creating from the received image multiple processed images that are applied to respective neural network (NN) models. Latent representations of the multiple processed images from each of the NN models are then fed to a combiner model trained to determine whether the latent representations indicate a phantom projection attack, and, responsively to a determination of a phantom projection attack, issuing a phantom projection indicator.

A method for determining authenticity of a document is provided that includes receiving, by an electronic device, an image of a document, assigning a label to the image, and obtaining vectors for each image in a subset of images. Each image is of a document and is assigned the same label as the received image. Moreover, the method includes encoding the received image into a vector, calculating a distance between the vector of the received image and each obtained vector, comparing each of the calculated distances against a threshold distance, and calculating a number of the calculated distances that are less than or equal to the threshold distance. In response to determining the calculated number is at least equal to a required number, the document in the received image is determined to be authentic. Otherwise, the received image requires manual review.

A counterfeit and imaging detection system includes a processor, a counterfeit product detection app, and a steganographic imaging model, electronically accessible by the counterfeit product detection app trained using image data and configured cause the processor to obtain a digital image of a physical product of a product line, the digital image captured by an imaging device and the digital image comprising pixel data, analyze the digital image to detect within the pixel data a batch code uniquely identifying a batch of the physical product of the product line, analyze the pixel data of the digital image to determine that the batch code is counterfeit, and augment a counterfeit list of batch codes to include the batch code, wherein the counterfeit list of batch codes remains electronically accessible to the counterfeit product detection app for one or more further counterfeit detection iterations.