Granted Patent B2

US 12,499,682 · App. 18/052,150 · Granted Dec 16, 2025

Inventors: Soumitri Naga Kolavennu (Plymouth, MN); Priyanka Singhal (Atlanta, GA); Varshini Sriram (Menlo Park, CA)

Assignee: U.S. Bank National Association

G06V20/44G06V10/768G06V20/52G06V40/161

Abstract

Claims (84)

1 . A method for detecting fraud device installation at an automated teller machine (ATM), comprising:

training a machine learning technique, the training comprising:

accessing one or more training videos,

converting frames of the one or more training videos into images,

adding artificial obstructions to a plurality of the images of the one or more training videos, including selecting a color, size, position, thickness, and opacity for the artificial obstructions, and

training the machine learning technique using the images of the one or more training videos;

receiving a video of an area around the ATM;

identifying one or more access events of the ATM based on frames of the video that indicate a detected object in the area;

evaluating frames of the video associated with the one or more access events using the machine learning technique to identify one or more obstructed frames that are obstructed by an object;

determining a predetermined number of frames from an access event of the one or more access events are partially obstructed based on the identified one or more obstructed frames;

determining a fraud device installation event occurred at the ATM based on the determination the predetermined number of frames from the access event are partially obstructed; and

assigning a flag to frames associated with the fraud device installation event.

2 . The method of claim 1 , further comprising:

creating images of frames of the video,

wherein:

identifying one or more access events based on the frames of the video that indicate the detected object includes evaluating the images;

identifying frames of the video associated with the one or more access events that are obstructed by the object using the machine learning technique includes providing the images to the machine learning technique as input; and

determining the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes evaluating the images.

3 . The method of claim 1 , wherein:

identifying frames of the video associated with the one or more access events that are obstructed includes using additional machine learning techniques; and

determining the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes determining at least two of (i) the machine learning technique, (ii) the additional machine learning techniques, or (iii) a combination of (i) and (ii) identify the predetermined number of frames from one of the access events as partially obstructed.

4 . The method of claim 1 , wherein the object is tape.

5 . The method of claim 1 , further comprising determining no transaction occurred during the access event, wherein determining the fraud device installation event occurred is based on the determination that no transaction occurred.

6 . The method of claim 1 , further comprising identifying a detected fraud device, wherein determining the fraud device installation event occurred is based on identifying the detected fraud device.

7 . The method of claim 1 , further comprising:

detecting a face of a person in one or more of the frames of the fraud device installation event; and

storing an image of the face of the person.

8 . The method of claim 7 , further comprising:

detecting the face of the person in a new video based on accessing the image of the person; and

determining a new fraud device installation event occurred based on detecting the face of the person in the new video.

9 . A system for video-based fraud detection, comprising:

a detection processor operable to:

train a machine learning technique, the training comprising:

accessing one or more training videos,

converting frames of the one or more training videos into images,

adding artificial obstructions to a plurality of the images of the one or more training videos, including selecting a color, size, position, thickness, and opacity for the artificial obstructions, and

training the machine learning technique using the images of the one or more training videos;

receive a video of an area around an automated teller machine (ATM);

identify one or more access events of the ATM based on frames of the video that indicate a detected object in the area;

evaluate frames of the video associated with the one or more access events using the machine learning technique to identify one or more obstructed frames that are obstructed by an object;

determine a predetermined number of frames from an access event of the one or more access events are partially obstructed based on the identified one or more obstructed frames;

determine a fraud device installation event occurred at the ATM based on the determination the predetermined number of frames from the access event are partially obstructed; and

assign a flag to frames associated with the fraud device installation event.

10 . The system of claim 9 , further comprising a video processor operable to:

create images of frames of the video,

wherein to:

identify one or more access events based on the frames of the video that indicate the detected object include to evaluate the images;

identify frames of the video associated with the one or more access events that are obstructed by the object using the machine learning technique includes to provide the images to the machine learning technique as input; and

determine the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes to evaluate the images.

11 . The system of claim 9 , wherein to:

identify frames of the video associated with the one or more access events that are obstructed includes to use additional machine learning techniques; and

determine the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes to determine at least two of (i) the machine learning technique, (ii) the additional machine learning techniques, or (iii) a combination of (i) and (ii) identify the predetermined number of frames from one of the access events as partially obstructed.

12 . The system of claim 9 , wherein the object is tape.

13 . The system of claim 9 , wherein the detection processor is further operable to determine no transaction occurred during the access event, wherein determining the fraud device installation event occurred is based on the determination that no transaction occurred.

14 . The system of claim 9 , wherein the detection processor is further operable to identify a detected fraud device, wherein determining the fraud device installation event occurred is based on identifying the detected fraud device.

15 . The system of claim 9 , wherein the detection processor is further operable to:

detect a face of a person in one or more of the frames of the fraud device installation event; and

store an image of the face of the person.

16 . The system of claim 15 , wherein the detection processor is further operable to:

detect the face of the person in a new video based on accessing the image of the person; and

determine a new fraud device installation event occurred based on the detection.

17 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

generate a machine learning technique by:

accessing one or more training videos;

converting frames of the one or more training videos into images;

adding artificial obstructions to a plurality of the images of the one or more training videos, including selecting a color, size, position, thickness, and opacity for the artificial obstructions; and

training the machine learning technique to detect fraud device installation using the plurality of the images of the one or more training videos; and

detect a fraud device installation event at an automated teller machine (ATM) by:

receiving a video of an area around the ATM;

identifying one or more access events of the ATM based on frames of the video that indicate a detected object in the area;

evaluating frames of the video associated with the one or more access events using the machine learning technique to identify one or more obstructed frames that are obstructed by an object;

determining a predetermined number of frames from an access event of the one or more access events are partially obstructed based on the identified one or more obstructed frames;

determining the fraud device installation event occurred at the ATM based on the determination the predetermined number of frames from the access event are partially obstructed; and

assigning a flag to frames associated with the fraud device installation event.

18 . The non-transitory computer-readable medium of claim 17 , wherein the non-transitory computer-readable medium stores further instructions that, when executed by the one or more processors, cause the one or more processors to:

create images of frames of the video,

wherein:

identifying one or more access events based on the frames of the video that indicate the detected object includes evaluating the images;

identifying frames of the video associated with the one or more access events that are obstructed by the object using the machine learning technique includes providing the images to the machine learning technique as input; and

determining the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes evaluating the images.

19 . The non-transitory computer-readable medium of claim 17 , wherein:

identifying frames of the video associated with the one or more access events that are obstructed includes using additional machine learning techniques; and

determining the fraud device installation event occurred based on determining that the predetermined number of frames from one of the access events are identified as partially obstructed includes determining at least two of (i) the machine learning technique, (ii) the additional machine learning techniques, or (iii) a combination of (i) and (ii) identify the predetermined number of frames from one of the access events as partially obstructed.

20 . The non-transitory computer-readable medium of claim 17 , wherein the object is tape.

Assignments (1)

Continuity (1)

References Cited (22)