Abstract: Systems, computer program products, and methods are described herein for monitoring and restricting electronic communication in a network based on communication characteristics. The method includes receiving an electronic communication from an end-point device. The method also includes identifying a potential user identity associated with the electronic communication. The potential user identity is based on one or more user identifiers associated with the electronic communication. The method further includes determining one or more restriction tags relating to the potential user identity. Each of the one or more restriction tags include a restriction to communication between the end-point device and one or more entity users. The method still further includes causing a restriction action to be executed based on determining the potential user identity has one or more restriction tags.

Abstract: Protection of sensitive data, such as Non-Public Information (NPI) in a video call/conference environment. In response to initiating a video call/conference amongst multiple call participants, Artificial Intelligence is implemented to monitor for detection of sensitive data in the video feed and/or audio feed being transmitted by a call participant. In response to detecting sensitive data in the video feed or the audio feed, the invention is configured to perform one or more actions that prevent one or more other call participants participating in the video call from viewing and/or hearing the sensitive data in the video feed or the audio feed. The sensitive data may be found in text indicia or images displayed in the video call field of view, individuals heard or seen in the background of the video call or the like.

Abstract: A system for sensitive data protection in a video call/conference environment. In response to initiating a video call/conference and placing a video call participant on mute, the system uses Artificial Intelligence (AI) specifically computer vision to monitor for mouth movements by the muted video call participant that indicate speech. In response to the monitoring detecting mouth movements that indicate speech, one or more actions are performed that prevent other video call/conference participants from viewing the mouth movement indicating speech by the first call participant. The actions may include stopping/pausing the video feed or capturing of video, obfuscating the region in the video feed that includes the muted video call participant's mouth, or using AI to replace the mouth movements with images of the video call participant's mouth being stationary. The actions may be performed once Non-Public Information (NPI) is identified in the speech.

Abstract: Systems, computer program products, and methods are described herein for auto-redacting data in a shared-GUI environment. The present invention is configured to receive an input request to transmit a data stream; analyze the available data to identify sensitive data; flag the sensitive data to generate flagged data; generate a dataset comprising data elements of the flagged data and location data of the flagged data; for each data element of the flagged data: analyze the data element to determine whether it is a sensitive data element; generate a label for the sensitive data element; and store the generated label, the sensitive data element, and the location data of the sensitive data element in a data structure; redact sensitive data elements from the available data to generate a redacted data stream; and transmit the redacted data stream.

Abstract: Systems, computer program products, and methods are described herein for auto-redacting data in a shared-GUI environment. The present invention is configured to receive an input request to transmit a continuous data stream; analyze the available data to identify sensitive data; flag the sensitive data to generate flagged data; generate a dataset comprising data elements of the flagged data and location data of the flagged data; for each data element of the flagged data: analyze the data element to determine whether it is a sensitive data element; generate a label for the sensitive data element; and store the generated label, the sensitive data element, and the location data of the sensitive data element in a data structure; redact sensitive data elements from the available data to generate a redacted continuous data stream; generate a prompt requesting acceptance of the redacted continuous data stream; and transmit the redacted continuous data stream.

Abstract: A smart card with a hard-wired tamper detection system and near-field communication is provided. The smart card may include a tamper detection unit (“TDU”) that may include a smart chip embedded in the smart card. The smart chip may be connected to a battery by two electrical wires. The smart chip may be connected to a simplified onboard tamper alerting system (“SOTAS”) by one electrical wire. The smart card may include a stripe scrambling film on a magnetic stripe of the smart card. The SOTAS may be configured to alert an issuer of the smart card in response to a trigger of a tamper detection, the trigger may be a signal received from the TDU in response to a removal of the smart chip from the smart card thereby causing each of the three wires to fracture and may render the smart chip inoperable.

Abstract: Systems, computer program products, and methods are described herein for auto-redacting data in a shared-GUI environment. The present invention is configured to receive an input request to transmit a continuous data stream; analyze the available data to identify sensitive data; flag the sensitive data to generate flagged data; generate a dataset comprising data elements of the flagged data and location data of the flagged data; for each data element of the flagged data: analyze the data element to determine whether it is a sensitive data element; generate a label for the sensitive data element; and store the generated label, the sensitive data element, and the location data of the sensitive data element in a data structure; redact sensitive data elements from the available data to generate a redacted continuous data stream; generate a prompt requesting acceptance of the redacted continuous data stream; and transmit the redacted continuous data stream.

Abstract: A self-service means for securely and efficiently destroying computing devices, specifically the permanent storage/memory devices/units within such computing devices. An apparatus, such as a kiosk receives a user's computing device and, in response, an image-capturing device(s) is activated to capture images of the computing device. The captured images serve as inputs to Machine Learning (ML) models that have been trained to determine/predict the location of permanent storage/memory devices/units within the computing devices. Once the location has been determined, a physical destruction element is activated at the determined/predicted location to destroy the permanent storage/memory devices/units.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax phone calls and videoconferences are provided. A program may record a target individual reciting exemplary phrases. The program may analyze the recordings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake audiovisual representations of the target individual. The program may store the data in a database. The program may analyze, in real-time, a phone call or videoconference to determine whether they are legitimate or illegitimate by comparing the audio or audiovisual contents of the phone call or videoconference with the exemplar deepfakes. When the program determines the phone call or videoconference is illegitimate, the program may terminate the call or videoconference and inform the recipient and others.

Abstract: Systems, and methods are described herein for integrated analysis of foreground and background communication data.

Abstract: Various aspects of the disclosure relate to automated monitoring of content associated with an individual for indicators that the content may be a deepfake. A deepfake analysis training device trains artificial intelligence-based models based on validated content associated with one or more profiles. Each profile is associated with an individual. A deepfake analysis engine may be installed on computing devices to monitor access to content (e.g., multimedia content) for deepfakes associated with the one or more profiles, as configured. The deepfake analysis engine may be stand-alone application, an application extension, or may be integrated into third-party applications via application programming interface functions. Analyzed content is assigned a likelihood score that is compared to threshold values that indicates a likelihood that the content is a deepfake. When identified, deepfake content initiates an alert generation and/or blocking of the content otherwise the content is presented to a user.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax accounts and posts on social media and elsewhere are provided. A program may gather verified images or writings of a target individual. The program may analyze the verified images and writings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake images or writings. The program may store the data in a database. The program may search a network for social media accounts or posts that may meet the baseline and determine whether they are legitimate or illegitimate by comparing the contents of the accounts or posts with the exemplar deepfakes. When the program determines the accounts or posts are illegitimate, the program may initiate a takedown of those accounts or posts. The program may use machine learning algorithms to refine itself and become more accurate.

Abstract: A system includes a user device and a computing system. The user device accesses a plurality of images captured by a camera and identifies, from the plurality of images, a particular image that depicts a portion of a user interaction object. The user device transmits the particular image across a wireless communications network. The computing system accesses the particular image depicting the portion of the user interaction object and determines a data item from the particular image. The computing system compares the data item to one or more accounts of the user and sends instructions to the user device to display an alert message on the electronic display based on the comparison of the data item to the one or more accounts.

Abstract: Systems, computer program products, and methods are described herein for detecting synthetic activity in a network via use of interactive artificial intelligence are provided. The method includes receiving a first account communication from an end-point device. The first account communication is associated with a user. The method also includes causing a transmission of a first reply communication based on the first account communication to be provided to the end-point device. The method further includes receiving a second account communication. The method still further includes determining a synthetic likelihood value based on the first account communication and/or the second account communication. The synthetic likelihood value indicates the likelihood that the first account communication and/or second account communication are synthetic.

Abstract: Systems, computer program products, and methods are described herein for monitoring and restricting electronic communication in a network based on communication characteristics. The method includes receiving an electronic communication from an end-point device. The method also includes identifying a potential user identity associated with the electronic communication. The potential user identity is based on one or more user identifiers associated with the electronic communication. The method further includes determining one or more restriction tags relating to the potential user identity. Each of the one or more restriction tags include a restriction to communication between the end-point device and one or more entity users. The method still further includes causing a restriction action to be executed based on determining the potential user identity has one or more restriction tags.

Abstract: Systems, computer program products, and methods are described herein for digital voice data processing and authentication. The present invention is configured to receive a user interaction comprising a digital audio signal and capture a first audio segment and a second audio segment of the digital audio signal. The first audio segment and the second audio segment are plotted into corresponding first and second plots. The first and second plots are compared, wherein comparing comprises subtracting the first plot from the second plot to form a difference plot. A quantity of outlier peaks is determined, then an artificial user probability is assigned to the user interaction, wherein the artificial user probability is low if the quantity of outlier peaks is greater than a predetermined outlier peak threshold. The artificial user probability is then displayed on a user interface of an endpoint device.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax accounts and posts on social media and elsewhere are provided. A program may gather verified images or writings of a target individual. The program may analyze the verified images and writings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake images or writings. The program may store the data in a database. The program may search a network for social media accounts or posts that may meet the baseline and determine whether they are legitimate or illegitimate by comparing the contents of the accounts or posts with the exemplar deepfakes. When the program determines the accounts or posts are illegitimate, the program may initiate a takedown of those accounts or posts. The program may use machine learning algorithms to refine itself and become more accurate.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax accounts and posts on social media and elsewhere are provided. A program may gather verified images or writings of a target individual. The program may analyze the verified images and writings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake images or writings. The program may store the data in a database. The program may search a network for social media accounts or posts that may meet the baseline and determine whether they are legitimate or illegitimate by comparing the contents of the accounts or posts with the exemplar deepfakes. When the program determines the accounts or posts are illegitimate, the program may initiate a takedown of those accounts or posts. The program may use machine learning algorithms to refine itself and become more accurate.

Abstract: Detecting targeted images, as well as variations thereof within computing networks, such as the Internet and the Metaverse. Navigation to predetermined websites and accessing the HTML code to search for image file extensions and, in some instances video file extensions within the listing of URLs. In response to finding URLs with image or video file extensions, the hyperlink is activated to navigate to the webpage containing the associated image or video and the image or video is downloaded for purposes of target image detection analysis. A virtual surveillance drone is deployed within virtual reality computing systems to navigate the entirety of the environment to search for images or, in some instances videos that may be displayed and, more specifically, images that appear to match or resemble the targeted image. The drone is capable of capturing the images and communicating the images to non-virtual computing systems for purposes of target image detection analysis.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax accounts and posts on social media and elsewhere are provided. A program may gather verified images or writings of a target individual. The program may analyze the verified images and writings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake images or writings. The program may store the data in a database. The program may search a network for social media accounts or posts that may meet the baseline and determine whether they are legitimate or illegitimate by comparing the contents of the accounts or posts with the exemplar deepfakes. When the program determines the accounts or posts are illegitimate, the program may initiate a takedown of those accounts or posts. The program may use machine learning algorithms to refine itself and become more accurate.