Regulatory Challenges in Synthetic Media Governance: Policy Frameworks for AI-Generated Content Across Image, Video, and Social Platforms

Abstract

Synthetic media, encompassing algorithmically generated images, videos, and text, has seen significant advancements with the proliferation of deep learning techniques, particularly generative adversarial networks and large language models. These technologies have democratized content creation, enabling both beneficial applications, such as personalized learning materials and creative entertainment, and harmful misuse, including deepfake-driven misinformation campaigns. The rapid pace of development in these areas presents considerable challenges for existing legal and regulatory frameworks. Policymakers, industry stakeholders, and civic institutions are pressed to address threats ranging from identity theft and defamation to national security risks. Yet constructing effective governance models for synthetic media remains a complex undertaking, as overregulation may hamper beneficial innovation while insufficient oversight can fuel malicious exploitation. This paper examines the core difficulties of regulating synthetic media, focusing on emergent risks and the evolving responsibilities of platform providers, application developers, and end-users. We discuss multiple policy approaches, from labeling requirements to algorithmic transparency mandates, and assess their efficacy in mitigating dangers without unduly restraining creative expression. By integrating insights from legal scholarship, technical developments, and social sciences, this research offers a nuanced view of how regulatory frameworks might evolve to address the multifaceted challenges of AI-generated content. Ultimately, we propose strategies for transparent oversight and balanced governance in this rapidly shifting technological landscape.