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, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.

Abstract: A system may include a processor and a non-transitory computer readable medium having stored thereon instructions for performing operations including obtaining a first dataset including a plurality of electronic documents and a plurality of entities, extracting, based on one or more rules, one or more subgraphs including representations between one or more electronic documents and one or more entities from the first dataset, identifying one or more groups in the one or more subgraphs, each group including electronic documents and entities selectively identified from a corresponding subgraph based on the representations, and learning the representations associated with the electronic documents and the entities based on the one or more groups and updating the representations in the first dataset. The first dataset may include data corresponding to contextual information associated with the plurality of entities and the representations may be determined based on this contextual information.

Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.

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: Techniques for predicting whether a submission includes a forged image. A computer system receives a submission from a user that includes an image and image metadata, such as an identifier for the user and a User-Agent string value. An image pixel embedding is generated from the image, and a profile embedding is generated from the image metadata. The image embedding is indicative of whether the image is similar to known image forgeries. The profile embedding is generated from a user activity embedding indicative of User-Agent values associated with the user identifier. The profile embedding is generated using a machine learning model that uses stored parameters to associate user activity, device information, and forgery groups. The profile embedding thus indicates whether the user is associated with known image forgeries. The image pixel embedding and profile embedding are then used by a neural network to output a forgery prediction.

Abstract: Systems, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.

Abstract: Systems and/or techniques for facilitating image forgery detection via headpose estimation may include a system that can receive a document from a client device. The system can identify, by executing a first trained machine learning model, an object that is depicted in the document. The system can determine, by executing a second trained machine learning model, a pose of the object. 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. The system can, in response to determining that the document is forged, transmit an unsuccessful validation message to the client device.

Abstract: Systems and/or techniques for facilitating image forgery detection via headpose estimation may include a system that can receive a document from a client device. The system can identify, by executing a first trained machine learning model, an object that is depicted in the document. The system can determine, by executing a second trained machine learning model, a pose of the object. 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. The system can, in response to determining that the document is forged, transmit an unsuccessful validation message to the client device.

Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.

Abstract: A method includes defining a first data vector of a first entity based on a set of data records associated with representative activities of the first entity, wherein the set of data records includes product data associated with the first entity, and an activities relationship between the first entity and a plurality of second entities. The method further includes defining a second data vector of the first entity based on supply chain topological connections between the second entities and the first entity, utilizing, a clustering space machine learning model to generate an entity vector representing the first entity based on the first data vector and the second data vector, and utilizing a classification machine learning model to generate an entity-specific classification of the first entity based on the entity vector.

Abstract: Methods and systems are presented for signed document image analysis and fraud detection. An image of a document may be received from a user's device. A first layer of a machine learning engine is used to identify a signature and a name of the user within different areas of the received image. A second layer of the machine learning engine is used to extract a plurality of features from the different areas of the image. The plurality of features includes at least one visual feature representing the signature and at least one textual feature representing the name. A combined feature representation of the signature and the name is generated based on the plurality of features extracted from the image. A third layer of the machine learning engine is used to determine whether the signature of the user has been digitally altered, based on the combined feature representation.

Abstract: A liveness detection system uses a combination of interactive and silent tests to verify physical presence of a live person at a client device. The system superimposes a box on a live video of the space in front of the client device and instructs the user to move to a position that places his or her face within the box. While the user's face is within the box, the system captures a sequence of frames from the video while an amount of illumination cast on the user's face is switched between bright and dark levels according to a random, semi-random, or predefined flash sequence. The system analyzes the resulting sequence of frames to confirm that the user's head pose is consistent with the location of the box, and that changes in brightness of the image of the user's face across the captured frames are consistent with the flash sequence.

Abstract: Methods and systems are presented for detecting physical tampering on a document based on analyzing an image of the document. When the image of the document is obtained, multiple contours are identified in the image based on pixel characteristics of the image. Dimension attributes of the contours are determined. Contours that are determined to correspond to borders or texts of the documents based on the dimension attributes are eliminated. A second text detection process based on a polygon method is performed on at least one remaining contour to determine whether the at least one remaining contour links multiple text elements together. The document is determined to have been physically manipulated when at least on contour remains in the image.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for user identification using Artificial Intelligence, Machine Learning, and data analytics. In one embodiment, a verification system and method is introduced that can provide user authentication using parallel modeling for face identification. The verification system used includes a face identification module for use in the identification and verification using parallel processing of a received image with a claimed identity. The parallel processing includes an ensemble of machine learning models processed in parallel for optimal performance.

Abstract: Techniques are disclosed relating to methods that include receiving, by a computer system, a plurality of images of an object taken from a video during which there is relative movement between the object and a camera that captures the video. The method may further include in response to determining that the video does not include a single image that meets a clarity threshold for the object, creating, by the computer system, a merged image of the object by combining portions of different images of the plurality of images such that the clarity threshold for the object is satisfied by the merged image. The method may also include capturing, by the computer system, information about the object using the merged image.

Abstract: A liveness detection system uses a combination of interactive and silent tests to verify physical presence of a live person at a client device. The system superimposes a box on a live video of the space in front of the client device and instructs the user to move to a position that places his or her face within the box. While the user's face is within the box, the system captures a sequence of frames from the video while an amount of illumination cast on the user's face is switched between bright and dark levels according to a random, semi-random, or predefined flash sequence. The system analyzes the resulting sequence of frames to confirm that the user's head pose is consistent with the location of the box, and that changes in brightness of the image of the user's face across the captured frames are consistent with the flash sequence.

Abstract: Systems, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.

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, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.