Information processing apparatus, control method of information processing apparatus, and program

Abstract

To provide a mechanism which can give identification information indicating that generated information is generated on the basis of a learning model, to the generated information when it is necessary that the identification information is given to the generated information.SOLUTION: An AI image generation server 101 constituted of an information processing apparatus comprises: generation means (generation unit 210) which generates information on the basis of a learning model; and information giving means (AI discrimination information giving unit 208) which gives, to the information generated by the generation means, discrimination information indicating that the generated information is generated on the basis of the learning model.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a control method for an information processing device, and a program. [Background technology]

[0002] In recent years, AI-based image generation technology has become widespread. This has enabled individuals to easily generate realistic images. However, malicious use of AI-based image generation technology has also become a problem. For example, photos of fictitious incidents or accidents involving real people or facilities have been fabricated. To prevent such misuse of AI-based image generation technology, various countries are urgently developing laws and development guidelines regarding AI-generated content. One example of such legislation is a law requiring AI-generated content to be clearly labeled as having been generated by AI. Furthermore, in the future, it is likely that a clear distinction between AI-generated content and other content will be required. Patent Document 1 discloses an image forming device that detects copyright-related read-protection information, such as copyright marks and logos, from image data of banknotes and securities, whose duplication is generally prohibited, and adds the detected information to the image data as information prohibiting duplication. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-211477 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the image forming apparatus described in Patent Document 1 does not provide information on whether an image has been generated using AI, even when such information is required.

[0005] The present invention has been made in view of the above problems. An object of the present invention is to provide a mechanism capable of imparting information indicating that the information is generated based on a learning model when such imparting is required for the information.

Means for Solving the Problems

[0006] In order to achieve the above object, an information processing apparatus of the present invention includes a generation unit that generates information based on a learning model, and an information imparting unit that imparts identification information indicating that the information generated by the generation unit is information generated based on the learning model to the information.

Effects of the Invention

[0007] According to the present invention, when it is required to impart information indicating that the information is generated based on a learning model to the information, such imparting can be performed.

Brief Description of the Drawings

[0008]

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Embodiments for Carrying Out the Invention

[0009] Hereinafter, each embodiment of the present invention will be described in detail with reference to the drawings. However, the configurations described in the following embodiments are merely examples, and the scope of the present invention is not limited by the configurations described in each embodiment. For example, each part constituting the present invention can be replaced with any configuration that can exhibit the same function. Also, any component may be added. Further, any two or more configurations (features) among the embodiments can be combined.

[0010] <First Embodiment> Hereinafter, the first embodiment will be described with reference to FIGS. 1 to 4. FIG. 1 is a block diagram showing the hardware configuration of an image processing system according to the first embodiment. As shown in FIG. 1, the image processing system 10 includes an AI image generation server 101 and a general-purpose terminal 102, which are communicably connected to each other via a network 100. The AI image generation server 101 is configured by an information processing device. This AI image generation server 101 can generate information based on a learning model (generation step). As the information, in this embodiment, it is image data of a still image or a moving image, but it is not limited to this. For example, it may be audio data, character data, etc., or data including at least one of these data. Hereinafter, generating image data based on a learning model (including a learned model) may be referred to as "AI image generation". This image data is transmitted to the general-purpose terminal 102. The general-purpose terminal 102 is a device capable of executing various processes on the image data transmitted from the AI image generation server 101. The general-purpose terminal 102 is not particularly limited, and for example, a desktop or notebook personal computer, a tablet terminal, a smartphone, etc. can be used.

[0011] Figure 2 is a block diagram showing the hardware configuration of the AI image generation server. As shown in Figure 2, the AI image generation server 101 includes a CPU 201, a RAM 202, a ROM 203, a storage unit 204, a GPU 207, an AI identification information adding unit (information adding means) 208, and a network I / F 209. The CPU 201 is a computer that controls the operation of the AI image generation server 101 based on the program expanded in the RAM 202. The ROM 203 is a boot ROM, and stores, for example, the boot program of the image processing system 10. Further, the ROM 203 also stores, for example, programs for causing the CPU 201 to execute each part and each means (control method of the information processing apparatus) of the AI image generation server 101. The storage unit 204 is a non-volatile device composed of an HDD, an SSD, etc. The storage unit 204 stores a learned model 205 used for AI image generation, an AI image generation program 206, and the like. In the present embodiment, the learned model 205 and the AI image generation program 206 function as a generation unit (generation means) 210 that performs AI image generation. Note that in AI image generation, any learned model 205 and an existing AI image generation program 206 such as Stable DI / Ffusion can be used, but the use is not limited thereto. The learned model 205 and the AI image generation program 206 are loaded into the RAM 202 and executed by the CPU 201. Since the technology related to AI image generation is a known technology, it is omitted here.

[0012] AI image generation is executed in response to a request for AI image generation from the general-purpose terminal 102. The CPU 201 instructs the GPU 207 to respond to this request. The GPU 207 performs AI image generation processing according to this instruction. As a result, image data is generated. The image data includes identification information that can identify that the image data is image data generated by AI image generation, that is, image data generated based on a learning model. The addition of identification information to the image data (information addition step) is performed by the AI identification information addition unit 208. The image data with the identification information added is transmitted from the network I / F 209 to the general-purpose terminal 102 via the network 100, or stored in the storage unit 204. Note that the network I / F 209 is connected to the network 100 and is responsible for input / output of various information. The connection between the network I / F 209 and the network 100 may be in a wired format or a wireless format.

[0013] The identification information attached to the image data is not particularly limited. For example, it includes information regarding that the image data is the output data output from the learned model 205, and information regarding the input data input to the learned model 205 when the output data is output. Other identification information includes, for example, the learned model 205, the program using the learned model 205, information regarding the probability that the image data is image data generated based on the learned model 205, information regarding the AI image generation request, etc. And at least one of these is attached as identification information. By this attachment, it is possible to identify that the image data is image data by AI image generation. The identification information may include information for specifying the AI-generated image part when not the entire image of the image data is an AI-generated image but a part of the image is an AI-generated image. As this identification information for the AI-generated image part, for example, it may be indicated by the upper left coordinate and the lower right coordinate of the image, or it may be pixel information constituting the AI-generated image part. In addition to the identification information, the image data may be attached with information regarding whether the image data is generated based on the learned model 205 authenticated by the rights holder, and information regarding whether the rights holder has authenticated the image data itself. In addition, the image data may be attached with information included in the AI image generation request from the general-purpose terminal 102, information regarding copyright when the image data by AI image generation is the subject of copyright, etc. Such attachment of other information is also performed by the AI identification information attachment unit 208. The image data includes image body data as main information visualized in the image and metadata as incidental information regarding the image body data. The AI identification information attachment unit 208 can attach the identification information to one of the image body data and the metadata. In the present embodiment, the AI identification information attachment unit 208 attaches the identification information to the metadata. Note that the attachment of the identification information to the image body data and the attachment of the identification information to the metadata may be switchable by an operation on the AI identification information attachment unit 208, or may be determined in advance for either one in the program.

[0014] Figure 3 is a block diagram showing the hardware configuration of a general-purpose terminal. As shown in Figure 3, the general-purpose terminal 102 has a CPU 301, a RAM 302, an SSD 303, a user I / F 304, and a network I / F 305. The CPU 301 controls the operation of the general-purpose terminal 102 based on the program developed in the RAM 302. Various programs and the like are stored in the SSD 303. This program includes, for example, a system program, an AI image generation application, and the like. The user I / F 304 has, for example, a display, a touch panel, a keyboard, a mouse, etc., and performs input / output processing for the user. The network I / F 305 is connected to the network 100 and is responsible for input / output of various information. The connection between the network I / F 305 and the network 100 may be in a wired format or a wireless format. Note that the general-purpose terminal 102 may have, for example, a telephone function, a camera function, and the like.

[0015] Figure 4 is a flowchart showing the processing executed by the general-purpose terminal and the AI image generation server. Figure 4(a) is a flowchart showing the processing executed by the general-purpose terminal. Figure 4(b) is a flowchart showing the processing executed by the AI image generation server. As shown in Figure 4(a), in step S401, the CPU 301 of the general-purpose terminal 102 determines that it has received, from the user via the user I / F 304, parameters regarding the image generated by the AI image generation server 101. This parameter is not particularly limited and is, for example, the same as those used in known AI image generation technologies, such as keywords, sentences, images, etc. related to the image.

[0016] In step S402, the CPU 301 transmits an image generation request based on the parameter in step S401 to the AI image generation server 101 via the network I / F 305.

[0017] As shown in Figure 4(b), in step S411, the CPU 201 of the AI image generation server 101 determines that it has received the image generation request transmitted in step S402 via the network I / F 209.

[0018] In step S412, the CPU 201 controls the GPU 207 to perform AI image generation using the learned model 205 or the like as described above.

[0019] In step S413, the CPU 201 controls the AI identification information adding unit 208 to add identification information to the metadata of the image data generated in step S412. Hereinafter, the image data obtained by AI image generation may be referred to as "AI image data". Also, the AI image to which the identification information is added may be referred to as "identification information added image data".

[0020] In step S414, the CPU 201 transmits the image data to which the identification information is added in step S413 to the general-purpose terminal 102 via the network I / F 209. As a result, the general-purpose terminal 102 can receive the identification information added image data.

[0021] As described above, in the AI image generation server 101, image data can be obtained by AI image generation (see step S412). And when it is necessary to add identification information indicating that the image data is obtained by AI generation to this image data, the addition can be performed (see step S413). The identification information added image data is received by the general-purpose terminal 102 via the network I / F 305 that functions as an acquisition means capable of acquiring the identification information added image data from the AI image generation server 101. And the identification information added image data is displayed as an image on the touch panel of the user I / F 304 of the general-purpose terminal 102. At this time, the user of the general-purpose terminal 102 can refer to the metadata of the image data and confirm the identification information added to the metadata on the touch panel. By this confirmation, it can be grasped that the image displayed on the touch panel includes an AI image. Thereby, for example, it is possible to once suspect that the AI image is a fake image.

[0022] <Second Embodiment> Hereinafter, with reference to FIGS. 5 to 7, the second embodiment will be described. The description will focus on the differences from the above-described embodiments, and the description of the same matters will be omitted. This embodiment is the same as the first embodiment except that the AI identification information adding unit adds identification information to the image body data among the image body data and metadata included in the image data. FIG. 5 is a flowchart showing the processing executed by the AI image generation server according to the second embodiment. In the flowchart shown in FIG. 5, the processing is executed in the order of step S411, step S412, step S501, and step S414. Steps S411, S412, and S414 are the same as steps S411, S412, and S414 in the flowchart shown in FIG. 4. In step S501, the CPU 201 of the AI image generation server 101 controls the AI identification information adding unit 208 to add identification information to the image body data of the image data generated in step S412. Thereby, the identification information added image data is obtained.

[0023] FIG. 6 is a diagram showing an example of an image of the image data. The image 600 shown in FIG. 6(a) is an image of the image body data included in the AI image data obtained by AI image generation in step S412. The image 601 shown in FIG. 6(b) is an image of the identification information added image data to which identification information is added to the image body data in step S501. The image 601 is an image in which an image frame 602 and the character 603 "AI" are superimposed as identification information indicating that it is an AI generated image. Thereby, when the user views the image 601 on the touch panel of the user I / F 304 of the general-purpose terminal 102, the user can grasp that the image 601 is an AI image. Note that the character 603 is "AI", but it is not limited to this, and any character indicating that it is an AI generated image may be used.

[0024] FIG. 7 is a diagram showing a modified example of an image of image data. The image 700 shown in FIG. 7(a) is an image of the image body data included in the AI image data obtained by AI image generation in step S412. This image 700 includes a partial image 702 that is an AI-generated image. The image 701A shown in FIG. 7(b) is an image of the identification information-added image data in which identification information is added to the partial image 702 in step S501. The image 701A includes a partial image 703. The partial image 703 is an image in which an image frame 703a and the character "AI" 703b are superimposed as identification information indicating that the partial image 702 is an AI-generated image. The image 701B shown in FIG. 7(c) is an image of the identification information-added image data in which identification information is added to the partial image 702 in step S501. The image 701B includes a partial image 704. The partial image 704 has a very light color over the whole or a mark such as a star shape superimposed as identification information indicating that the partial image 702 is an AI-generated image. The image 701C shown in FIG. 7(d) is an image of the identification information-added image data in which identification information is added to the partial image 702 in step S501. The image 701C includes a partial image 705. The partial image 705 is an image filled with black as identification information indicating that the partial image 702 is an AI-generated image. Then, by checking the partial image 703 of the image 701A, the partial image 704 of the image 701B, and the partial image 705 of the image 701C, the user can grasp that the images 701A to 701C all include AI images. Note that, as identification information indicating that it is an AI-generated image, for example, invisible information such as an electronic watermark may be superimposed on the whole image or a partial image.

[0025] <Third Embodiment> Hereinafter, the third embodiment will be described with reference to FIGS. 8 to 10. The description will focus on the differences from the above-described embodiments, and the description of the same matters will be omitted. This embodiment is the same as the first embodiment except that the generation unit that performs AI image generation and the addition unit that adds identification information are incorporated in different devices from each other.

[0026] FIG. 8 is a block diagram showing the hardware configuration of the general-purpose terminal according to the third embodiment. The general-purpose terminal 800 shown in FIG. 8 is a device that is communicably connected to the AI image generation server 101, which is an external device, in the same manner as in the above-described embodiments. In this embodiment, the general-purpose terminal 800 further includes an AI image identification unit (determination means) 801 and an AI identification information addition unit (information addition means) 802 in addition to a CPU 301, a RAM 302, an SSD 303, a user I / F 304, and a network I / F 305. The network I / F 305 can acquire existing image data generated by the AI image generation server 101 from the AI image generation server 101 (acquisition step). This image data includes AI image data obtained by AI image generation before identification information is added, identification information addition image data in a state where identification information is added to the AI image data, and non-AI image data not generated by AI image generation.

[0027] The AI image identification unit 801 determines whether the image data acquired by the network I / F 305 is image data generated by AI image generation (determination step). This determination is made based on the presence or absence of identification information in the image data. Further, the CPU 301 can function as a quantification means for quantifying the probability, that is, the certainty indicating the likelihood that the image data acquired by the network I / F 305 is data obtained by AI image generation, in numerical values such as probability or score. In this case, the AI image identification unit 801 converts the certainty into a percentage, and when the numerical value of the certainty is equal to or higher than a threshold value (N%), it determines that the image data is image data generated by AI image generation. Also, when the numerical value of the certainty is less than the threshold value (N%), the AI image identification unit 801 determines that the image data is not image data generated by AI image generation. The threshold value may be stored in advance in the SSD (storage means) 303, or may be set as appropriate via the user I / F (operation means) 304. Further, it is preferable that the threshold value can be changed as appropriate via the user I / F 304. The AI identification information adding unit 802 can perform the same processing as the AI identification information adding unit 208 of the AI image generation server 101. Specifically, when it is determined by the AI image identification unit 801 that the image data is image data generated by AI image generation as a result of the determination, the AI identification information adding unit 802 can add identification information to the image data (information addition step). Further, the AI identification information adding unit 802 may add the certainty.

[0028] Figure 9 is a flowchart showing the processing (AI image identification processing) executed by the general-purpose terminal. As shown in Figure 9, in step S900, the CPU 301 determines whether an identification start button (not shown) for determining whether the image data acquired by the network I / F 305 is image data generated by AI image generation has been operated, that is, pressed. The identification start button is provided, for example, on the user interface 304. As a result of the determination in step S900, if it is determined that the identification start button has been operated, the process proceeds to step S901. On the other hand, as a result of the determination in step S900, if it is determined that the identification start button has not been operated, the process waits at step S900.

[0029] In step 901, the CPU 301 controls the AI image identification unit 801 to determine whether identification information is attached to the image data acquired by the network I / F 305. If, as a result of the determination in step 901, it is determined that the identification information is attached, the process ends. In this case, the image data acquired by the network I / F 305 is stored in the SDD 303 in its original state. On the other hand, if, as a result of the determination in step 901, it is determined that the identification information is not attached, the process proceeds to step S903.

[0030] In step S903, the CPU 301 controls the AI image identification unit 801 to determine whether the image data acquired by the network I / F 305 is image data generated by AI image generation. If, as a result of the determination in step S903, it is determined that the image data is image data generated by AI image generation, the process proceeds to step S904. On the other hand, if, as a result of the determination in step S903, it is determined that the image data is not image data generated by AI image generation, the process ends. In this case, the image data acquired by the network I / F 305 is stored in the SDD 303 in its original state.

[0031] In step S904, the CPU 301 controls the AI identification information attachment unit 802 to attach identification information to the image data acquired by the network I / F 305. The destination of the attachment of the identification information may be the image body data, the metadata, or both the image body data and the metadata. After step S904 is executed, the process ends. The image data with the attached identification information is stored in the SDD 303. Note that in the flowchart shown in FIG. 9, the processing order of step S901 and step S903 may be interchanged, that is, reversed.

[0032] Also, as described above, there are three types of existing image data obtained from the AI image generation server 101. The first is AI image data obtained by AI image generation before identification information is assigned. The second is identification information - attached image data in a state where identification information is assigned to the AI image data. The third is non - AI image data not obtained by AI image generation. And in the determination in step S901, the first type of image data is determined as "No", the second type of image data is determined as "Yes", and the third type of image data is determined as "No". Also, in the determination in step S903, the first type of image data is determined as "Yes", and the third type of image data is determined as "No".

[0033] Figure 10 is a flowchart showing the processing (AI image notification processing) executed on the general - purpose terminal. Here, it is assumed that the user interface 304 has a speaker. As shown in Figure 10, in step S1000, the CPU 301 determines whether a button (not shown) for opening the image data stored in the SDD 303 in the processing process of the flowchart shown in Figure 9 has been operated. This button is provided on the user interface 304, for example. As a result of the determination in step S1000, if it is determined that the button has been operated, the process proceeds to step S1001. On the other hand, as a result of the determination in step S1000, if it is determined that the button has not been operated, the process waits at step S1000.

[0034] In step S1001, the CPU 301 controls the AI image identification unit 801 to determine whether the image data to be opened in step S1000 is image data generated by AI image generation. This determination is made based on the presence or absence of identification information. As a result of the determination in step S1001, if it is determined that the image data is image data generated by AI image generation, the process proceeds to step S1002. On the other hand, as a result of the determination in step S1001, if it is determined that the image data is not image data generated by AI image generation, the process ends.

[0035] In step S1002, the CPU 301 controls the speaker of the user interface 304 to audibly notify that the image data to be opened in step S1000 is image data generated by AI image generation. By this notification, the user can grasp that the image data is image data generated by AI image generation before opening the image data. Note that the notification targets in step S1002 are the image data stored in the SDD 303 after the execution of step 901 and the image data stored in the SDD 303 after the execution of step 904. Further, the notification by the user interface 304 is not limited to audible notification, and may be, for example, notification by an image, light emission, vibration, etc., or notification combining these. Also, after the execution of step S1002, the CPU 301 may determine whether to forcibly open the image data to be opened in step S1000. This determination can be made, for example, based on whether an operation is performed on a button provided on the user interface 304 for forcibly opening the image data. When the operation of the button is performed, the image data will be forcibly opened.

[0036] As described above, the preferred embodiments of the present invention have been explained. However, the present invention is not limited to the above-described embodiments, and various modifications and changes are possible within the scope of the gist thereof. The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors of a computer of the system or device read and execute the program. Further, the present invention can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

[0037] The disclosure of each embodiment includes the following configurations, methods, and programs. (Configuration 1) A generation means for generating information based on a learning model, An information processing apparatus comprising information imparting means for imparting, to the information generated by the generation means, identification information indicating that the information is information generated based on the learning model. (Configuration 2) An acquisition unit that acquires information, A determination unit that determines whether or not the information acquired by the acquisition unit is information generated based on a learning model, An information processing apparatus, comprising: an information addition unit that, when it is determined by the determination unit that the information acquired by the acquisition unit is information generated based on the learning model, adds identification information indicating that the information is information generated based on the learning model to the information acquired by the acquisition unit. (Configuration 3) The information processing apparatus is a device communicably connected to an external device including a generation unit that generates the information based on the learning model, The acquisition unit is capable of acquiring the information from the external device, the information processing apparatus according to Configuration 2. (Configuration 4) A quantification unit that quantifies the probability that the information is information generated based on the learning model, The determination unit determines that the information is information generated based on the learning model when the value quantified by the quantification unit is greater than or equal to a threshold value, and determines that the information is not information generated based on the learning model when the value is less than the threshold value, the information processing apparatus according to Configuration 2 or 3. (Configuration 5) A storage unit that stores the threshold value, An operation unit that performs an operation to change the threshold value, the information processing apparatus according to Configuration 4. (Configuration 6) The information includes main information visualized as an image and metadata as additional information related to the main information, The information addition unit adds the identification information to the main information, the information processing apparatus according to any one of Configurations 1 to 5. (Configuration 7) The information includes main information visualized as an image and metadata as additional information related to the main information, The information addition unit adds the identification information to the metadata, the information processing apparatus according to any one of Configurations 1 to 5. (Configuration 8) The information providing means, as the identification information, provides at least one of the fact that the information is output data output from the learning model, the input data input to the learning model when the output data is output, the learning model, a program using the learning model, and the probability that the information is information generated based on the learning model, and the information processing apparatus according to any one of Configurations 1 to 7. (Configuration 9) The information processing apparatus according to any one of Configurations 1 to 8, further comprising a notification means for notifying that the information is information generated based on the learning model when the identification information is provided by the information providing means. (Configuration 10) The information processing apparatus according to any one of Configurations 1 to 9, wherein the information includes at least one of still image or moving image data, audio data, and character data. (Method 1) A method for controlling an information processing apparatus, comprising: a generation step of generating information based on a learning model; an information providing step of providing the information generated in the generation step with identification information indicating that the information is information generated based on the learning model, and a control method for an information processing apparatus. (Method 2) A method for controlling an information processing apparatus, comprising: an acquisition step of acquiring information; a determination step of determining whether the information acquired in the acquisition step is information generated based on a learning model; an information providing step of providing the information acquired in the acquisition step with identification information indicating that the information is information generated based on the learning model when it is determined in the determination step that the information acquired in the acquisition step is information generated based on the learning model, and a control method for an information processing apparatus. (Configuration 13) A program for causing a computer to execute each means of the information processing apparatus according to any one of Configurations 1 to 10.

Explanation of Reference Numerals

[0038] 101 AI Image Generation Server 102 General-Purpose Terminal 205 Trained Model 208 AI Identification Information Assignment Unit 210 Generation Unit (Generation Means) 305 Network I / F 801 AI Image Identification Unit 802 AI Identification Information Assignment Unit

Claims

1. generation means for generating information based on a learning model; An information processing apparatus comprising information imparting means for imparting identification information indicating that the information is information generated based on the learning model to the information generated by the generation means.

2. acquisition means for acquiring information; judgment means for judging whether or not the information acquired by the acquisition means is information generated based on a learning model; When it is judged by the judgment means that the information acquired by the acquisition means is information generated based on the learning model, the information acquired by the acquisition means is provided with identification information indicating that the information is information generated based on the learning model. An information processing apparatus comprising information imparting means.

3. The information processing apparatus is a device communicably connected to an external device including generation means for generating the information based on the learning model, The information processing apparatus according to claim 2, wherein the acquisition means can acquire the information from the external device.

4. quantification means for quantifying the probability that the information is information generated based on the learning model; The judgment means judges that the information is information generated based on the learning model when the value quantified by the quantification means is equal to or greater than a threshold value, and judges that the information is not information generated based on the learning model when the value is less than the threshold value. The information processing apparatus according to claim 2.

5. storage means for storing the threshold value; The information processing apparatus according to claim 4, further comprising operation means for performing an operation of changing the threshold value.

6. The information includes main information visualized as an image and metadata as additional information related to the main information, The information processing apparatus according to claim 1 or 2, wherein the information imparting means imparts the identification information to the main information.

7. The information includes main information visualized as an image and metadata as additional information related to the main information, The information processing apparatus according to claim 1 or 2, wherein the information imparting means imparts the identification information to the metadata.

8. The information providing means, as the identification information, provides at least one of the fact that the information is output data output from the learning model, input data input to the learning model when the output data is output, the learning model, a program using the learning model, and the probability that the information is information generated based on the learning model. The information processing apparatus according to claim 1 or 2, characterized in that.

9. The information processing apparatus according to claim 1 or 2, further comprising notification means for notifying that the information is information generated based on the learning model when the identification information is provided by the information providing means.

10. The information processing apparatus according to claim 1 or 2, characterized in that the information includes at least one of still image or moving image data, audio data, and character data.

11. A method for controlling an information processing apparatus, comprising: a generation step of generating information based on a learning model; An information providing step of providing the information generated in the generation step with identification information indicating that the information is information generated based on the learning model. A method for controlling an information processing apparatus, characterized in that.

12. A method for controlling an information processing apparatus, comprising: an acquisition step of acquiring information; a determination step of determining whether the information acquired in the acquisition step is information generated based on a learning model; If, as a result of the determination in the determination step, it is determined that the information acquired in the acquisition step is information generated based on the learning model, the information acquired in the acquisition step is provided with identification information indicating that the information is information generated based on the learning model. A method for controlling an information processing apparatus, characterized in that it comprises an information providing step.

13. A program for causing a computer to execute each means of the information processing apparatus according to claim 1 or 2.

Images