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: Systems and methods for user-agent anomaly detection are disclosed. In one embodiment, a user-agent string may be embedded into a numerical data vector representation using a sentence embedding algorithm (e.g., FastText). A predictive score may be calculated based on the numerical data vector representation and using a probability distribution function model that models a likelihood of occurrence of the observed user-agent based on patterns learned from historic payload data (e.g., a Gaussian Mixture Model). The predictive score may be compared to a threshold and, based on the comparison, it may be determined whether the user-agent is fraudulent.

Abstract: Systems and methods for emulator detection are disclosed. In one embodiment, a user agent string may be embedded into a first numerical data vector representation. Hardware characteristics of a client device corresponding to the user agent may be embedded into a second numerical data vector representation. Based on the first numerical data vector representation of the user agent and the second numerical data vector representation of the hardware characteristics, and their consistency, the client device may be determined to be an emulator or a non-emulator device.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Techniques are disclosed relating to determining whether to authorize a requested action based on whether an entity is an automated computer. In some embodiments, a computer system tracks, at a user interface of a computing device, a sequence of pointer movements. The computer system maps, based on coordinate locations of pointer movements in the sequence, respective movements in the sequence to a plurality of functional areas. Based on the mapping, the computer system generates a movement graph and determines, based on the movement graph, whether an entity associated with the sequence of pointer movements is an automated computer. In response to receiving a request to authorize an action at the computing device, the computer system generates, based on the determining, an authorization decision for the action and transmits the authorization decision to the computing device. Determining whether the entity is an automated computer may advantageously prevent fraudulent activity.

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: 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/techniques for facilitating context-enhanced category classification are provided. In various embodiments, a system can access a first textual description of a product or service. In various aspects, the system can identify, via execution of named entity recognition, one or more keywords in the first textual description. In various instances, the system can access, from a set of queryable databases, one or more second textual descriptions that respectively correspond to the one or more keywords. In various cases, the system can generate, via execution of word embedding, a first numerical representation of the first textual description and one or more second numerical representations of the one or more second textual descriptions. In various aspects, the system can identify, via execution of a machine learning classifier, a category label for the product or service, based on the first numerical representation and the one or more second numerical representations.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Systems and methods for emulator detection are disclosed. In one embodiment, a user agent string may be embedded into a first numerical data vector representation. Hardware characteristics of a client device corresponding to the user agent may be embedded into a second numerical data vector representation. Based on the first numerical data vector representation of the user agent and the second numerical data vector representation of the hardware characteristics, and their consistency, the client device may be determined to be an emulator or a non-emulator 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: Systems and methods for user-agent anomaly detection are disclosed. In one embodiment, a user-agent string may be embedded into a numerical data vector representation using a sentence embedding algorithm (e.g., FastText). A predictive score may be calculated based on the numerical data vector representation and using a probability distribution function model that models a likelihood of occurrence of the observed user-agent based on patterns learned from historic payload data (e.g., a Gaussian Mixture Model). The predictive score may be compared to a threshold and, based on the comparison, it may be determined whether the user-agent is fraudulent.

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.

Abstract: Techniques are disclosed relating to determining whether document objects included in an image correspond to known document types. In some embodiments, a computing system maintains information specifying a set of known document types. In some embodiments, the computing system receives an image that includes objects. In some embodiments, the computing system analyzes, using a first neural network, the image to identify a document object and location information specifying a location of the document object within the image. In some embodiments, the computing system determines, using a second neural network, whether the document object within the image corresponds to a document type specified in the set of known document types, where the determining is performed based on the location information of the document object. In some embodiments, disclosed techniques may assist in automatically extracting information from documents, which in turn may advantageously decrease processing time for onboarding new customers.

Abstract: Disclosed in one aspect is a pickled vegetable-based edible packaging paper, the raw material of which is composed of the following components in parts by weight: 92-95 parts of a high dietary fiber-level pickled vegetable, a compounding thickener (0.6-0.8 parts of pectin, 0.6-0.8 parts of potato starch, and 0.2-0.4 parts of soy protein), and a compounding plasticizer (3-4 parts of sorbitol, and 0.5-1.5 parts of citral); and further disclosed in another aspect is a method for preparing a pickled vegetable-based edible packaging paper. The pickled vegetable paper of the present invention is a novel pickled vegetable-based product having both edible and practical functions, which product is rich in dietary fibers, retains the unique flavor and color of the pickled vegetable, has toughness and non-stickiness to teeth, and is soft and easy to process after being wetted.

Abstract: There are provided systems and methods for intelligent clustering of account communities for account feature adjustment. A user may utilize an online account with the service provider to process transactions electronic. However, initially the account may be unverified or otherwise untrusted, and limits on electronic transaction processing may be imposed on the account. To provide intelligent feature adjustment, the service provider may utilize a machine learning technique to cluster verified accounts into communities based on their data representations. Thereafter, when a transaction by an unverified account violates a limit imposed on the unverified account, the service provider may process the unverified account using a machine learning model trained for account correlations to the community clusters in order to determine a corresponding community cluster of accounts and adjust a feature based on behaviors and traits of the verified accounts in the community cluster.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Techniques are disclosed relating to determining whether document objects included in an image correspond to known document types. In some embodiments, a computing system maintains information specifying a set of known document types. In some embodiments, the computing system receives an image that includes objects. In some embodiments, the computing system analyzes, using a first neural network, the image to identify a document object and location information specifying a location of the document object within the image. In some embodiments, the computing system determines, using a second neural network, whether the document object within the image corresponds to a document type specified in the set of known document types, where the determining is performed based on the location information of the document object. In some embodiments, disclosed techniques may assist in automatically extracting information from documents, which in turn may advantageously decrease processing time for onboarding new customers.