Digital Communication Strategies for Coping with DeepFake Content Distribution

With the rapid development of generative artificial intelligence (AI) technology, DeepFake technology has triggered a wide-ranging discussion on authenticity of digital content and misuse of the technology worldwide. Although DeepFake technology has demonstrated positive applications in the fields of film and television entertainment, education, medical rehabilitation, and cultural heritage protection, its misuse has led to social problems such as reputation infringement, telecommunication fraud, and public opinion manipulation. Aiming at this status quo, this paper analyzes the existing detection means, including methods based on local texture and deep feature fusion, facial natural texture degree recognition, geometric feature analysis, and Generative Adversarial Network (GAN) adversarial detection, and explores their performances and shortcomings in recognition accuracy and generalization ability. At the same time, a multifaceted response strategy is proposed: creators are required to follow ethical protocols to embed digital watermarks, users are actively involved in monitoring through reporting mechanisms, platforms achieve content traceability and responsibility tracking through digital fingerprinting, and technicians optimize the multimodal detection model and promote open source and ease of use. This paper emphasizes the combination of technical means and institutional constraints to achieve multi-party collaboration to build a healthy and transparent digital communication ecosystem, and to jointly address the risk of DeepFake content dissemination.