The Problem of Insufficient Regulatory Framework for the Use of Images Created Using Artificial Intelligence Technologies in the Media Industry

This article examines the challenges and risks associated with the rapid development and improvement of deepfake technology, which is becoming increasingly accessible and difficult to distinguish from genuine content. Examples of technological advances and their applications are provided. Aim. To substantiate the need for legal regulation of the use of images synthesized using artificial intelligence in the media industry, the importance of international dialogue on legislation, the codification of prohibitions on the use of images of officials and the dissemination of deepfakes, and the establishment of liability for their creation and dissemination. Tasks. To identify and formulate areas for public authorities to focus their efforts in ensuring the protection of individual rights and freedoms in the context of digitalization, as well as the protection of media content emanating from public authorities and their officials. Methods. The challenge lies in determining the areas of public authorities’ efforts and coordinating their international efforts in the area of information protection (media content emanating from them), which manifests itself in the distortion of the state’s true intentions in the implementation of foreign policy. The article examines the forms of liability for the illegal use of deepfakes and measures to combat this phenomenon, including the introduction of mandatory labeling of official photos and videos using steganographic methods for identification and verification, and the creation of legal mechanisms to protect copyright and prevent the illegal use and distribution of counterfeit materials. Results and conclusions. Public authorities should work to align the wills of states with the goal of developing an appropriate international convention and integrating key principles for combating deepfakes into national legislation.

1. Berlin, S. I., Bathory, G. A., Kopylova, D. V. Biometrics in the Banking Sector. Studying the Question of Security of the Storages of Biometrics Data // Bulletin of the Academy of Knowledge. 2019. No. 3 (32). (In Russ.) EDN: QRSWNV

2. Vozhegov, A. V. The Concept of Artificial Neural Networks // Actual Modern Problems and Promising Branches of Innovative Development. Ekaterinburg : Scientific Research Center Technical Innovations, 2021. No. 7. P. 256–259. (In Russ.) EDN: KRLCXG

3. Gasanova, R. B. Identification Value of Voice Messages for Investigation of Crimes // Law and Power. 2021. No. 2. P. 43–46. (In Russ.) EDN: JRFRYG

4. Kirova, L. M., Makarevich, M. L. Legal Aspects of the Use of Neural Networks // Innovative Economy: Prospects for Development and Improvement. 2018. No. 1 (27). P. 58–63. (In Russ.) EDN: YQCXYK

5. Laptev, V. A. Deepfake and Other Artificial Inntelligence Products on the Way to the Development of Online Justice // Actual Problems of Russian Law. 2021. No. 11 (132). P. 180–186. (In Russ.) EDN: HOMQCG. DOI: 10.17803/1994-1471.2021.132.11.180-186

6. Maslova, E. V. The Russian Unified Biometric System: How Safe Is It for the Banking Sector? // Modern Problems and Prospects for Development of the Banking Sector: Materials of the IV All-Russian Scientific and Practical (Correspondence) Conference / ed. by Y. Y. Radyukova. Tambov : Tambov State University named after G. R. Derzhavin, 2019. P. 76–83. (In Russ.) EDN: LXJEIJ

7. Sukhan, A. A. Generative Adversarial Neural Networks in Trend Determination Tasks // Moscow Economic Journal. 2019. No. 6. P. 32. (In Russ.) EDN: BGTFHF. DOI: 10.24411/2413-046X-2019-16031

8. Fedorov, M. V. Socio-economic aspects of the introduction of artificial intelligence technologies // Journal of Digital Economy Research. 2023. Vol. 1, No. 1. P. 6–60. (In Russ.) EDN: MGIURM. DOI: 10.24833/14511791-2023-1-6-60

9. Khalturin, E. D., Makarets, A. B. Generative Adversarial Networks: Combining Neural Networks to Stimulate Learning and Ease Computational Load // Mathematics and Mathematical Modeling: Collection of Materials of the XIII All-Russian Youth Scientific and Innovative School. Sarov : Intercontact, 2019. P. 297–299. (In Russ.) EDN: ZOFOXD

10. Getman, A. A., Ling Yilan. The Deepfake Technology: Threats or Opportunities for Customs // Administrative Consulting. 2023. No. 4 (172). P. 30–36. EDN: TWWJO. DOI: 10.22394/1726-1139-2023-4-30-36