KR-20260067719-A - METHOD AND APPARATUS FOR FAKE FINGERPRINTS GENERATION, AND TRAINING METHOD OF ARTIFICIAL INTELLIGENCE FOR IDENTIFYING FAKE FINGERPRINTS

Abstract

A method for generating a forged fingerprint according to a first aspect of the present invention comprises the steps of: acquiring a fingerprint image targeting a predetermined finger; extracting unique identification information of the fingerprint from the fingerprint image; acquiring generation constraints intended for diversifying the generated training fingerprint image; and providing the unique identification information and the generation constraints to a training fingerprint image generation model to acquire a training fingerprint image to be used for training a forged fingerprint detection model. At this time, the unique identification information includes a feature shared by the actual fingerprint of the finger and the training fingerprint image corresponding to the actual fingerprint.

Inventors

• 김범준
• 이종만
• 신호철
• 박재현
• 강영묵

Assignees

• 주식회사 슈프리마

Dates

Publication Date

20260513

Application Date

20241106

Claims (19)

1. In a method for generating a forged fingerprint performed by a fingerprint generation device, A step of acquiring a fingerprint image targeting a specific finger; A step of extracting unique identification information of a fingerprint from the fingerprint image above; A step of obtaining generation constraints aimed at diversifying the generated training fingerprint images; and The method includes the step of providing the unique identification information and the generation constraints to a training fingerprint image generation model to obtain a training fingerprint image to be used for training a forged fingerprint detection model, The above unique identification information is, Features shared by the actual fingerprint of the finger and the training fingerprint image for the actual fingerprint. Method for generating forged fingerprints.
2. In paragraph 1, The above fingerprint image is, A captured image of the actual fingerprint or an image obtained from the training fingerprint image generation model Method for generating forged fingerprints.
3. In paragraph 2, The above unique identification information is, at least one of first identification information based on the start and end points and branching points of a ridge within a fingerprint of a finger, second identification information based on the direction of the ridge, and third identification information based on the distribution density of the ridge. Method for generating forged fingerprints.
4. In paragraph 1, The above generation constraint is, Information regarding a sensor for identifying a fingerprint, information regarding the surrounding environment in which the fingerprint is identified, and labeling information regarding the fingerprint Method for generating forged fingerprints.
5. In paragraph 4, The above labeling information is, Information including whether the above fingerprint is the actual fingerprint or a forged fingerprint, and information regarding the means of forgery Method for generating forged fingerprints.
6. In paragraph 1, The above forged fingerprint is, The one obtained from the above finger through at least one means of silicone, latex, paper, and glue Method for generating forged fingerprints.
7. In paragraph 1, The above-mentioned fingerprint image generation model for training is, The one trained to generate different training fingerprint images according to conditions included in the generation constraints while the above-mentioned unique identification information is frozen. Method for generating forged fingerprints.
8. In paragraph 1, The above method is, It further includes a step of acquiring random noise, and The above-mentioned fingerprint image generation model for training is, Even if the above generation constraints are the same, different training fingerprint images are generated according to the above random noise Method for generating forged fingerprints.
9. In a method for training a forged fingerprint detection model performed by a learning device of an artificial intelligence model, A step of acquiring a learning fingerprint image generated according to the above claim 1; and A step comprising training the forged fingerprint detection model using at least one of the acquired training fingerprint image and the image acquired from the actual fingerprint. Training method for a forged fingerprint detection model.
10. In Paragraph 9, The above fingerprint image is, A captured image of the actual fingerprint or an image obtained from the training fingerprint image generation model Training method for a forged fingerprint detection model.
11. In Paragraph 10, The above unique identification information is, at least one of first identification information based on the start and end points and branching points of a ridge within a fingerprint of a finger, second identification information based on the direction of the ridge, and third identification information based on the distribution density of the ridge. Training method for a forged fingerprint detection model.
12. In Paragraph 9, The above generation constraint is, Information regarding a sensor for identifying a fingerprint, information regarding the surrounding environment in which the fingerprint is identified, and labeling information regarding the fingerprint Training method for a forged fingerprint detection model.
13. In Paragraph 12, The above labeling information is, Information including whether the above fingerprint is the actual fingerprint or a forged fingerprint, and information regarding the means of forgery Training method for a forged fingerprint detection model.
14. In Paragraph 9, The above forged fingerprint is, The one obtained from the above finger through at least one means of silicone, latex, paper, and glue Training method for a forged fingerprint detection model.
15. In Paragraph 9, The above-mentioned fingerprint image generation model for training is, The one trained to generate different training fingerprint images according to conditions included in the generation constraints while the above-mentioned unique identification information is frozen. Training method for a forged fingerprint detection model.
16. In Paragraph 9, The above method It further includes a step of acquiring random noise, and The above-mentioned fingerprint image generation model for training is, Even if the above generation constraints are the same, different training fingerprint images are generated according to the above random noise Training method for a forged fingerprint detection model.
17. Memory capable of storing computer-executable instructions; and By executing the above command, a fingerprint image targeting a specific finger is obtained, and Extract unique identification information of the fingerprint from the above fingerprint image, and Obtain generation constraints aimed at diversifying the generated training fingerprint images, and A processor that provides the unique identification information and the generation constraints to a training fingerprint image generation model to obtain a training fingerprint image to be used for training a forged fingerprint detection model, wherein The above unique identification information is, Features shared by the actual fingerprint of the finger and the training fingerprint image for the actual fingerprint. Fingerprint generation device.
18. A computer-readable recording medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, A step of acquiring a fingerprint image targeting a specific finger; A step of extracting unique identification information of a fingerprint from the fingerprint image above; A step of obtaining generation constraints aimed at diversifying the generated training fingerprint images; and The method includes the step of providing the unique identification information and the generation constraints to a training fingerprint image generation model to obtain a training fingerprint image to be used for training a forged fingerprint detection model, The above unique identification information is, A method in which the processor performs a method including features shared by the actual fingerprint of the finger and the training fingerprint image for the actual fingerprint. Computer-readable recording medium.
19. As a computer program stored on a computer-readable recording medium, When the above computer program is executed by a processor, A step of acquiring a fingerprint image targeting a specific finger; A step of extracting unique identification information of a fingerprint from the fingerprint image above; A step of obtaining generation constraints aimed at diversifying the generated training fingerprint images; and The method includes the step of providing the unique identification information and the generation constraints to a training fingerprint image generation model to obtain a training fingerprint image to be used for training a forged fingerprint detection model, The above unique identification information is, Instructions for the processor to perform a method including features shared by the actual fingerprint of the finger and the training fingerprint image for the actual fingerprint. A computer program stored on a computer-readable recording medium.

Description

Method and apparatus for generating fake fingerprints, and method of training an artificial intelligence model for identifying fake fingerprints The present invention relates to a method and apparatus for generating a forged fingerprint, and a method for training an artificial intelligence model for detecting a forged fingerprint. Fingerprint recognition is one of the most widely used biometric technologies in modern times, rapidly replacing passwords consisting of characters or patterns in everyday life. While many people perceive fingerprint technology as being more difficult to leak or duplicate than characters or patterns, fingerprints are not immune to duplication; therefore, if a fingerprint is exposed to others and duplicated, authentication could still be possible using a forged, duplicated fingerprint. Meanwhile, fingerprints can pose a security vulnerability as they are easily exposed to the outside world, such as being left on every object touched. Accordingly, the development of technologies to distinguish between real and forged fingerprints (for example, the development of forged fingerprint detection technology using artificial intelligence) is actively underway. However, there are limitations in improving the performance of forged fingerprint detection models due to insufficient training data to train conventional AI algorithms for identifying forged fingerprints. FIG. 1 is a block diagram exemplarily illustrating a forged fingerprint generating device according to one embodiment. Figure 2 is a block diagram exemplifying the function of a forged fingerprint generation program. Figure 3 is an example diagram showing the relationship between the unique identification information extraction unit, the training fingerprint image acquisition unit, and the model training unit of a forged fingerprint generation program. FIG. 4 is a flowchart exemplarily showing the operation of the model learning unit of a forged fingerprint generation program according to one embodiment. FIG. 5 is a flowchart exemplarily showing the operation of a learning fingerprint image acquisition unit of a forged fingerprint generation program according to one embodiment. FIG. 6 is a flowchart exemplarily illustrating a method for generating a forged fingerprint according to one embodiment. FIG. 7 is an example diagram showing, in its entirety, a method for training a fingerprint image generation model according to one embodiment and a method for generating a fingerprint image for training by the model thus trained. FIG. 8 is an example diagram showing a method for training a forged fingerprint detection model according to one embodiment, and FIG. 9 is an example diagram showing a method for training a forged fingerprint detection model according to another embodiment. The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but can be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. In describing the embodiments of the present invention, specific descriptions of known functions or configurations will be omitted if it is determined that such detailed descriptions could unnecessarily obscure the essence of the invention. Furthermore, the terms described below are defined in consideration of their functions in the embodiments of the present invention, and these definitions may vary depending on the intentions or practices of the user or operator. Therefore, such definitions should be based on the content throughout this specification. The terms used in this specification will be briefly explained, and the invention will be described in detail. The terms used in this specification have been selected to be as widely used as possible, taking into account the functions of the present invention; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this invention should be defined not merely by their names, but based on their meanings and the overall content of the invention. When a part of a specification is described as 'comprising' a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Additionally, the term "part" as used in the specification refers to software or hardware