AI and Deepface Influencers: The Challenge of Authenticity in the Online Space

Samoilenko Vladyslava

Citation: Samoilenko Vladyslava, "AI and Deepface Influencers: The Challenge of Authenticity in the Online Space", Universal Library of Engineering Technology, Volume 02, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article investigates the phenomenon of AI influencers generated with DeepFake technologies and examines their impact on the authenticity of digital content, audience trust, and the economic models of marketing. It outlines the technological foundations—generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformers, text-to-speech systems, and voice conversion—and proposes a classification of AI influencers by modality (visual, audio, textual, and multimodal). The methodological framework rests on a comparative analysis of previous studies in the field, which identifies authenticity challenges in the online environment arising from advances in artificial intelligence and the proliferation of deepfakes. To counter these threats, the study recommends multimodal detectors of synthetic content and legal measures such as mandatory labeling, international certification standards, and legislative adaptation. The conclusion offers guidance on developing real-time detection methods, harmonizing legal regulation, and expanding media-literacy programs in the “post-truth” era. The findings will interest an interdisciplinary community of scholars and practitioners—from digital-ethics theorists and media sociologists to specialists in artificial intelligence and marketing—who seek a deeper understanding of the transformation of identity and trust in the age of software-generated personas. Legislators and platform developers shaping regulatory and technological mechanisms for verifying online content authenticity will likewise find the results valuable.


Keywords: AI-Influencers, DeepFake, Online Trust, Synthetic Content Detection, GAN, Legal Regulation, Media Literacy.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0202004