SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI

• URL: http://arxiv.org/abs/2411.11478v2
• Date: Tue, 19 Nov 2024 14:11:22 GMT
• Title: SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI
• Authors: Kui Ren, Ziqi Yang, Li Lu, Jian Liu, Yiming Li, Jie Wan, Xiaodi Zhao, Xianheng Feng, Shuo Shao,
• Abstract summary: AIGC watermarks offer an effective solution to mitigate malicious activities. We provide a taxonomy based on the core properties of the watermark. We discuss the functionality and security threats of AIGC watermarking.
• Score: 24.187726079290357
• License:
• Abstract: The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious activities. However, existing watermarking surveys focus more on traditional watermarks, overlooking AIGC-specific challenges. In this work, we propose a systematic investigation into AIGC watermarking and provide the first formal definition of AIGC watermarking. Different from previous surveys, we provide a taxonomy based on the core properties of the watermark which are summarized through comprehensive literature from various AIGC modalities. Derived from the properties, we discuss the functionality and security threats of AIGC watermarking. In the end, we thoroughly investigate the AIGC governance of different countries and practitioners. We believe this taxonomy better aligns with the practical demands for watermarking in the era of GenAI, thus providing a clearer summary of existing work and uncovering potential future research directions for the community.

Related papers

• Watermarking Large Language Models and the Generated Content: Opportunities and Challenges [18.01886375229288]
  generative large language models (LLMs) have raised concerns about intellectual property rights violations and the spread of machine-generated misinformation. Watermarking serves as a promising approch to establish ownership, prevent unauthorized use, and trace the origins of LLM-generated content. This paper summarizes and shares the challenges and opportunities we found when watermarking LLMs.
  arXiv  Detail & Related papers  (2024-10-24T18:55:33Z)
• Watermarking Recommender Systems [52.207721219147814]
  We introduce Autoregressive Out-of-distribution Watermarking (AOW), a novel technique tailored specifically for recommender systems. Our approach entails selecting an initial item and querying it through the oracle model, followed by the selection of subsequent items with small prediction scores. To assess the efficacy of the watermark, the model is tasked with predicting the subsequent item given a truncated watermark sequence.
  arXiv  Detail & Related papers  (2024-07-17T06:51:24Z)
• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• Watermark-based Attribution of AI-Generated Content [34.913290430783185]
  We conduct the first systematic study on watermark-based, user-level attribution of AI-generated content. Our key idea is to assign a unique watermark to each user of the GenAI service and embed this watermark into the AI-generated content created by that user. Attribution is then performed by identifying the user whose watermark best matches the one extracted from the given content.
  arXiv  Detail & Related papers  (2024-04-05T17:58:52Z)
• ACW: Enhancing Traceability of AI-Generated Codes Based on Watermarking [5.782554045290121]
  It is desirable to know whether a piece of code is generated by AI, and which AI is the creator. Watermarking is broadly considered a promising solution and is successfully applied for identifying AI-generated text. We propose ACW (AI Code Watermarking), a novel method for watermarking AI-generated codes.
  arXiv  Detail & Related papers  (2024-02-12T09:40:18Z)
• Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
  We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context. Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
  arXiv  Detail & Related papers  (2023-07-25T20:24:22Z)
• A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions [19.50785795365068]
  AIGC uses generative large AI algorithms to assist humans in creating massive, high-quality, and human-like content at a faster pace and lower cost. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm.
  arXiv  Detail & Related papers  (2023-05-25T15:09:11Z)
• Evading Watermark based Detection of AI-Generated Content [45.47476727209842]
  A generative AI model can generate extremely realistic-looking content. Watermark has been leveraged to detect AI-generated content. A content is detected as AI-generated if a similar watermark can be decoded from it.
  arXiv  Detail & Related papers  (2023-05-05T19:20:29Z)
• AI-Generated Content (AIGC): A Survey [4.108847841902397]
  artificial intelligence-generated content (AIGC) has emerged to address the challenges of digital intelligence in the digital economy. This paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features.
  arXiv  Detail & Related papers  (2023-03-26T02:22:12Z)
• A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? [112.12974778019304]
  generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. This work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc.
  arXiv  Detail & Related papers  (2023-03-21T10:09:47Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.