Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities
Lele Cao
TL;DR
The paper addresses the challenge of distinguishing AI-generated content from human-created material by surveying watermarking techniques across text, visual, and audio modalities. It defines watermarking as a formal encoding/decoding/verification framework $(\mathcal{E},\mathcal{D},\mathcal{V})$ and analyzes imperceptibility, robustness, security, and capacity, with metrics such as BER $= \frac{1}{N}\sum_{i=1}^{N}\mathbb{1}(m_i\neq\hat{m}_i)$. It catalogs text approaches (probabilistic token-level, lexical/syntactic, contextual/semantic), visual methods (spatial, frequency, hybrid, DL-based), and audio strategies (speech and music), highlighting strengths and vulnerabilities under transformations and adversarial attacks. The authors emphasize the need for standardization, privacy-preserving designs, and hybrid, adaptive techniques to counteract evolving GenAI capabilities, proposing retrieval-based and cross-modal verification as promising directions. Overall, watermarking is positioned as a foundational, proactive mechanism to enhance content authenticity and trust in a landscape of increasingly capable GenAI systems.
Abstract
The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio modalities and critically evaluate existing approaches based on their effectiveness, robustness, and practicality. Additionally, we identify key challenges, including resistance to adversarial attacks, lack of standardization across different content types, and ethical considerations related to privacy and content ownership. Finally, we discuss potential future research directions aimed at enhancing watermarking strategies to ensure content authenticity and trustworthiness. This survey serves as a foundational resource for researchers and practitioners seeking to understand and advance watermarking techniques for AI-generated content detection.