Abstract: A method for enhancing detection of fraudulent authentication data is provided that includes obtaining within a window of time from one or more sensors a plurality of signals. Each signal includes data for a biometric modality of a person. Moreover, the method includes determining whether the biometric modality data in the obtained signals was captured contemporaneously. If so, a feature vector is computed for the biometric modality data in each of the plurality of signals, each feature vector is encoded into a corresponding qubit register using an encoding algorithm, and a multi-qubit entangled state is generated from the qubit registers. An entanglement indicator value is also generated for the multi-qubit entangled state and the entanglement indicator value is compared against a threshold value. When the entanglement indicator value satisfies the threshold value, the obtained biometric modality data is determined to be of a live person.

Abstract: A method of training a machine learning model to conduct authentication transactions is provided that includes the steps of obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and information for a passphrase spoken by the respective user and belongs to a same or different data class. Each data class includes a user identity and a passphrase identifier. Moreover, the method includes the steps of creating, using a machine learning model being trained, at least one embedding for each audio signal. The machine learning model includes parameters. Furthermore, the method includes calculating, by a machine learning algorithm using the embeddings, a loss, and updating parameters of the machine learning model based on the calculated loss.

Abstract: A method of maintaining behavioral integrity of an artificial intelligence (AI) agent includes capturing AI agent execution signals. The AI agent is bound to a person. Moreover, the method includes calculating similarity scores from the execution signals and an agent similarity score from the similarity scores, verifying runtime attestation of an execution environment hosting the artificial intelligence agent, and calculating an attestation conformity score from the runtime attestation. Furthermore, the method includes calculating an integrity score by combining the similarity and attestation conformity scores and evaluating policy-as-code at one or more policy checkpoints to yield a decision outcome. The policy checkpoints include discover, invoke, and runtime. The method also includes comparing the integrity score against a threshold value.

Abstract: A method for enhancing detection of multimedia data generated using artificial intelligence is provided that includes the step of receiving, by an electronic device operated by a user, multimedia data. The multimedia data includes at least one of one or more images and voice data. Moreover, the method includes the step of analyzing, using a trained machine learning model, the one or more images for artifacts indicating the use of artificial intelligence in generating the one or more images, and determining, using a synthetic speech detection algorithm, whether the voice data includes artifacts indicating the use of artificial intelligence in generating the voice data. In response to determining the one or more images includes at least one artifact or the voice data includes at least one artifact, the multimedia data is determined to be fraudulent and originating from artificial intelligence generation.

Abstract: A method for enhancing detection of morphed biometric modality data is provided that includes receiving, by an electronic device, biometric modality data of a person, extracting feature vectors from the biometric modality data, normalizing the feature vectors, encoding the normalized feature vectors into qubits, and expanding, using at least one quantum algorithm, the normalized feature vectors into a high-dimensional space. Moreover, the method includes generating a distribution from the high-dimensional space based on the qubits, calculating a deviation between the generated distribution and a corresponding record high-dimensionality feature vector distribution of the person, and comparing the calculated deviation against a threshold deviation value. In response to determining the deviation satisfies the threshold deviation value, the method determines the received biometric modality data was morphed.

Abstract: A method of maintaining fidelity of a bond between a person and an artificial intelligence agent bound to the person includes capturing data for behavioral modalities of a person, calculating a score for each behavioral modality, calculating a similarity score from the calculated scores, and comparing the similarity score against the threshold value. In response to determining the similarity score fails to satisfy the threshold value, the method substitutes a snapshot of a Baseline Persona Model of the person for the Baseline Persona Model. The Baseline Persona Model is a machine learning model that encodes the behavioral modality data of a person over time. The snapshot is a copy of the Baseline Persona Model at an instant in time. Subsequently data for the behavioral modalities of the person is captured over time. The Baseline Persona Model is retrained using a set of the subsequently captured data approved by the person.

Abstract: A method for enhancing detection of fraudulent authentication data includes receiving, by an electronic device, data during an authentication transaction, computing a feature vector from the received data, and normalizing the feature vector. The method also includes encoding the normalized feature vector into qubits, expanding, using at least one quantum algorithm, the normalized feature vector into a high-dimensional space, and detecting in the high-dimensional space anomalies indicative of fraud based on the qubits. Furthermore, the method includes calculating, based on the detected anomalies, a confidence score reflecting a likelihood that the received data is genuine and comparing the confidence score against a threshold value. In response to determining the confidence score fails to satisfy the threshold value, the method determines that the received data requires secondary authentication.

Abstract: A method of training a machine learning model (MLM) to conduct authentication transactions includes obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and an audio data production flag and belongs to a same or different data class. Moreover, the method includes creating, using a MLM being trained, at least one embedding for each audio signal and calculating, using the at least one embedding, a loss for a machine learning algorithm used for training the MLM. Furthermore, the method includes updating parameters of the MLM based on the calculated loss. In response to determining criteria defining an end of training have been satisfied, deeming the MLM to be operable for use in simultaneously successfully verifying the identity of a user based on voice biometric data and verifying the voice biometric data was spoken by a living person.

Abstract: A method for determining authenticity of a document is provided that includes the step of capturing, by an electronic device, an image of a document. The document includes image data of a biometric modality of a user and informational data. Moreover, the method includes the steps of cropping the captured image data to include an image of the document only, removing the informational data from the cropped image, and generating a gray scale image from the cropped image. The gray scale image and the cropped image are a pair of images. Furthermore, the method includes the step of simultaneously processing, by a trained machine learning model (MLM) each image in the pair of images, to determine whether the identity document in the captured image is authentic.

Abstract: A method of creating an algorithm to enhance detection of fraudulent audio data is provided that includes generating, by an electronic device, audio data using a synthetic speech creation algorithm, processing the generated audio data using a synthetic speech detection algorithm, determining whether the synthetic speech detection algorithm detects the generated audio data, generating additional audio data using the synthetic speech creation algorithm, and creating a database including the generated audio data and genuine data. When the synthetic speech detection algorithm failed to detect the generated audio data, the synthetic speech detection algorithm is divided into a first algorithm and a second algorithm. The second algorithm is fine-tuned using the data in the created database.

Abstract: 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.

Abstract: A method for enhancing detection of synthetic speech is provided that includes the step of receiving, by an electronic device, voice biometric data of a user captured while the user was speaking and analyzing the context in which the received voice biometric data was captured. The context includes environmental and situational factors. Moreover, the method includes the steps of analyzing characteristics of the received voice biometric data for anomalies associated with synthetic speech, generating a risk score based on the results of the analysis, and comparing the risk score against a threshold value. In response to determining the risk score fails to satisfy the threshold score, the method includes a step of determining the captured voice biometric data includes anomalies associated with synthetic speech and initiating an alert protocol.

Abstract: A method for enhancing detection of fraudulent data is provided that includes the step of analyzing, by an electronic device, voice biometric data captured while the user was speaking to determine whether the captured voice biometric data exhibits anomalies associated with fraudulent voice audio data. Moreover, the method includes the steps of analyzing circumstances under which the voice biometric data was captured, analyzing mannerisms of the user while the voice biometric data was captured, and calculating a risk score based on at least the results of the analyses. Furthermore, the method includes the steps of comparing the risk score against a threshold value, and in response to determining the risk score fails to satisfy the threshold value generating an alert having a risk level corresponding to the risk score.

Abstract: A method for enhancing detection of synthetic voice data is provided that includes converting, by an electronic device, monophonic voice data into stereophonic voice data. The stereophonic voice data includes a first channel signal and a second channel signal. Moreover, the method includes decomposing, by a trained machine learning model, the stereophonic voice data into a mid-signal and a side signal. The method also includes determining artifacts indicative of synthetic generation in the structured and secondary artifacts, calculating, based on the determined artifacts, a probability score reflecting the likelihood the monophonic voice data was synthetically generated, and comparing the probability score against a threshold value. When the probability score satisfies the threshold value, there is a high likelihood that the monophonic voice data includes synthetic artifacts, and an alert is generated indicating the monophonic voice data is potentially fraudulent.

Abstract: A method for enhancing detection of a fraudulent identity document in an image is provided that includes receiving, by an electronic device, an image of an identity document associated with a user including at least one background object. The method also includes determining the size and orientation of the at least one background object based on the received image, extracting information about the received image from the received image, and extracting information from the received image about a capture device that captured the received image. Each of the size and orientation of the at least one background object, the extracted received image data information, and the extracted capture device information is compared against corresponding information in record data of the user. A similarity score is calculated for each comparison. When each similarity score satisfies a threshold value, the identity document in the received image is deemed to be fraudulent.

Abstract: A method for enhanced access control is provided that includes the steps of displaying buttons, by an electronic device, where each button corresponds to a different service. Moreover, the method includes receiving, by the electronic device, input regarding a selected service, transmitting at least one credential for the selected service to a computer, and capturing, by a camera in communication with the computer, facial image data of a user. The method also includes determining whether the facial image data was taken of a live person. In response to determining the facial image data was taken of a live person, a verification transaction is conducted based on the at least one credential and facial image data. In response to verifying the identity of the user as true, the user is granted access to the selected service.

Abstract: A method of host-directed illumination for verifying the validity of biometric data of a user is provided that includes capturing biometric data from a user with an authentication device during authentication and directing illumination of the biometric data from a host authentication system during the capturing operation. Moreover, the method includes comparing illumination characteristics of the captured biometric data against illumination characteristics expected to result from the directing operation, and determining that the user is a live user when the illumination characteristics of the captured biometric data match the illumination characteristics expected to result from the directing operation.

Abstract: A method for enhancing user liveness detection is provided that includes receiving image data of a user that includes items of metadata. Moreover, the method includes comparing each item of metadata associated with the received image data against a corresponding item of metadata associated with record image data of the user, and determining whether each item of metadata associated with the received image data matches the corresponding item of metadata. In response to determining at least one item of metadata associated with the received image data does not match the corresponding item of metadata, the method deems the received image data to be genuine and from a live person. In response to determining all items of metadata associated with the received image match the corresponding item of metadata, the method deems the received image data to be fraudulent and not from a living person.

Abstract: A method for enhanced authorization to access transportation hub services is provided that includes creating, by an electronic device, a token based on credentials of a traveler. Moreover, the method includes displaying the token by the electronic device for scanning for accessing a security service area and determining whether the credentials are valid. In response to determining the credentials are valid, a traveler operating the electronic device is authorized to access the security service area. Otherwise, the traveler is denied access to the security service area.

Abstract: A method for authenticating a user is provided that includes capturing, by a computing device, key stroke data generated as a result of a user typing content into the computing device, and calculating feature values from the key stroke data. The method also includes calculating distance scores from the feature values and average distance scores from the distance scores, and entering the average distance scores into a classifier. The user is successfully authenticated when the classifier verifies the identity of the user.