用扩散模型提升图像水印的隐蔽性与抗攻击能力
Visual Watermarking in the Era of Diffusion Models: Advances and Challenges
- 利用扩散模型学习特征，嵌入难以察觉的鲁棒水印
- 相比传统方法，水印在图像篡改后仍可识别
- 适合数字版权保护与反伪造场景
随着Stable Diffusion等生成式AI技术的发展，视觉内容面临更严重的滥用风险，引发版权侵权担忧。视觉水印作为有效保护机制，可声明所有权并阻止未经授权使用。传统深度伪造检测多依赖被动技术，难以应对复杂篡改。而扩散模型通过有效学习特征，显著提升检测精度，并支持嵌入不可感知且鲁棒的水印。本文分析了与扩散模型相关的水印技术的优势与挑战，重点关注其鲁棒性及在水印生成中的应用。通过探索先进扩散模型与水印安全的融合，旨在推动针对不断演进的伪造威胁，保持水印鲁棒性的研究进展。强调开发创新方案以保护数字内容、维护所有权权利的重要性。
原文摘要 · Abstract (English)
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.
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