UDC 378.1

Berman N.D.¹ (Khabarovsk, Russian Federation) – nina.berman@mail.ru

¹Pacific National University

Abstract. With the rapid development of synthetic media technologies (generative adversarial networks (GANs), diffusion models, and voice cloning technologies), the problem of “total deepfakes”—highly realistic multimodal fakes virtually indistinguishable from authentic content—has become particularly pressing. Traditional approaches to information security and media literacy, focused on identifying text fakes and falsified photographs, are becoming ineffective. Total deepfakes undermine the foundations of trust in visual and audio data, change the mechanisms of information dissemination, and require a revision of existing approaches to content verification at the institutional and individual levels. The aim of this study is to present a model for developing competencies in information security and anti-fake literacy in the context of total deepfakes. This paper draws on research findings on the psychology of perception and cognitive biases, as well as data on modern deepfake technologies. The model proposed in this paper describes the components of anti-fake literacy in the context of pervasive deepfakes and how resilience develops gradually, from the intuitive to the reflexive-proactive level. Its implementation will contribute to increasing the resilience of students and society as a whole to the threats posed by the spread of synthetic media. The developed recommendations can be integrated into educational programs, digital literacy training, and professional development systems.

Keywords: deepfake, synthetic content, information security, media literacy, antifake literacy, critical thinking, generative adversarial networks.

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For citation: Berman N. D. (2026). Model for the development of competencies in the field of information security and anti-fake literacy in the context of total deepfake. CITISE, 2, 448-457. (In Russian).