Visual Watermarking in the Era of Diffusion Models: Advances and Challenges

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Authors: Junxian Duan, Jiyang Guan, Wenkui Yang, Ran He

Published: 2025-05-13 03:14:18+00:00

AI Summary

This research paper analyzes the use of diffusion models for visual watermarking to protect against copyright infringement and misuse of generative AI. It explores the strengths and challenges of integrating diffusion models with watermarking techniques, focusing on robustness and watermark generation.

Abstract

As generative artificial intelligence technologies like Stable Diffusion advance, visual content becomes more vulnerable to misuse, raising concerns about copyright infringement. Visual watermarks serve as effective protection mechanisms, asserting ownership and deterring unauthorized use. Traditional deepfake detection methods often rely on passive techniques that struggle with sophisticated manipulations. In contrast, diffusion models enhance detection accuracy by allowing for the effective learning of features, enabling the embedding of imperceptible and robust watermarks. We analyze the strengths and challenges of watermark techniques related to diffusion models, focusing on their robustness and application in watermark generation. By exploring the integration of advanced diffusion models and watermarking security, we aim to advance the discourse on preserving watermark robustness against evolving forgery threats. It emphasizes the critical importance of developing innovative solutions to protect digital content and ensure the preservation of ownership rights in the era of generative AI.

Key findings

The paper identifies the strengths and limitations of different diffusion model-based watermarking approaches. It highlights the trade-off between watermark effectiveness and image quality, and the vulnerability of some methods to denoising and image manipulation techniques. Future research directions are proposed focusing on improving computational efficiency, robustness, and multi-attribution capabilities.

Approach

The paper analyzes existing visual watermarking techniques within the context of diffusion models. It categorizes these methods into data-driven passive, sampling-driven passive, and adversarial proactive approaches, examining their effectiveness and robustness against attacks.

Datasets

UNKNOWN

Model(s)

Various diffusion models (DMs), including Latent Diffusion Models (LDMs) and Stable Diffusion, are referenced and analyzed in the context of watermarking, but no specific models are used in the main contribution of the paper itself.

Author countries

China

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