What is it about?
AI-generated content (AIGC)—like realistic text, images, and videos—offers incredible creativity but also poses serious risks, such as spreading misinformation and disinformation. Our comprehensive review dives into the latest technologies designed to detect AIGC. We categorize these into two main types: 'External Detection' (identifying content created by AI) and 'Internal Detection' (addressing inherent flaws like AI 'hallucinations' or biases before content is generated). We examine how these detection methods have evolved across all forms of media, from text to multimodal content, and discuss the publicly available tools. We also highlight the major challenges—like keeping up with rapidly evolving AI and ensuring detection methods are robust and fair. Ultimately, this research provides a crucial roadmap for building a safer, more trustworthy digital ecosystem, helping everyone from researchers to policymakers understand and combat the risks of misleading AI-generated content.
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Why is it important?
Our work stands out as the first truly systematic and comprehensive literature review to cover the entire spectrum of AI-generated content detection—from text and images to video, audio, and multimodal outputs. This is highly timely given the rapid proliferation of diverse AI models. We introduce a novel, unified classification taxonomy that not only identifies AI-generated content but also addresses inherent AI flaws like 'hallucinations' and biases, offering a clearer path for future research. The key difference this makes is providing a unified resource and practical roadmap for researchers, policymakers, and industry professionals. By synthesizing current knowledge, highlighting critical challenges, and outlining future directions, our paper is instrumental in guiding the development of robust, ethical AI content forensics, ultimately securing a more trustworthy digital ecosystem against misinformation and disinformation.
Perspectives
For me, leading this systematic review has been a profound experience, deeply highlighting the critical need for vigilance in our AI-driven world. The 'cat-and-mouse game' between AI generation and detection is relentless, and the potential for misuse—from deepfakes to misinformation—is genuinely concerning. My greatest hope for this publication is that it serves as more than just an academic resource; I want it to be a clear call to action. I hope it empowers not only researchers to innovate smarter solutions, but also informs policy makers, journalists, and even the general public about the immense challenges and the collective effort required to ensure AI-generated content is used responsibly and safely. We are all stakeholders in the future of digital information, and this paper is my contribution to fostering a more secure and trustworthy AI ecosystem.
WENPENG MU
Shanghai Jiao Tong University
Read the Original
This page is a summary of: Advancements in AI-Generated Content Forensics: A Systematic Literature Review, ACM Computing Surveys, August 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3760526.
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