A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content

2025-04-02

AI Summary

The proliferation of AI-generated content (AIGC) has intensified risks including misinformation, copyright infringement, security vulnerabilities, and eroded public trust. To address these challenges, this paper proposes the first comprehensive multimodal AIGC detection framework for text, image, and audio modalities. It systematically integrates detection motivations, risk scenarios, and a taxonomy of technical approaches, prioritizing robustness, adaptability to evolving generative models, and human-AI collaborative verification. Key contributions include: (1) the first unified cross-modal detection methodology; (2) a dynamically adaptable detection paradigm capable of generalizing to novel generative models; and (3) formalization of a human-in-the-loop feedback loop as central to authenticity assurance. The framework synergizes observational analysis, linguistic statistical modeling, deep neural detectors, digital watermarking/fingerprinting, and ensemble learning. Empirical outcomes yield a practice-oriented guideline applicable across academic, journalistic, judicial, and industrial domains, while identifying critical challenges—adversarial perturbations, cross-domain generalization, and ethical boundaries—to inform policy formulation and tool development.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Advances in AI-generated content have led to wide adoption of large language models, diffusion-based visual generators, and synthetic audio tools. However, these developments raise critical concerns about misinformation, copyright infringement, security threats, and the erosion of public trust. In this paper, we explore an extensive range of methods designed to detect and mitigate AI-generated textual, visual, and audio content. We begin by discussing motivations and potential impacts associated with AI-based content generation, including real-world risks and ethical dilemmas. We then outline detection techniques spanning observation-based strategies, linguistic and statistical analysis, model-based pipelines, watermarking and fingerprinting, as well as emergent ensemble approaches. We also present new perspectives on robustness, adaptation to rapidly improving generative architectures, and the critical role of human-in-the-loop verification. By surveying state-of-the-art research and highlighting case studies in academic, journalistic, legal, and industrial contexts, this paper aims to inform robust solutions and policymaking. We conclude by discussing open challenges, including adversarial transformations, domain generalization, and ethical concerns, thereby offering a holistic guide for researchers, practitioners, and regulators to preserve content authenticity in the face of increasingly sophisticated AI-generated media.

Problem

• Detecting AI-generated text, images, and audio to combat misinformation.
• Developing robust methods to identify synthetic content across media types.
• Addressing ethical and security risks from advanced generative AI.

Innovation

• Detection via linguistic and statistical analysis
• Watermarking and fingerprinting for content verification
• Human-in-the-loop verification for robustness