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English(EN) DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory

新的DRIFT攻击以98-100%的成功率移除扩散模型水印

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为DRIFT的新方法，用于移除嵌入在扩散模型生成图像中的水印。这种黑盒攻击通过部分重新加噪图像，然后使用随机反向重采样来探索替代生成路径。DRIFT通过识别水印被拒绝的第一个点并保留该拒绝信息来有效绕过水印验证，在无需访问密钥或内部验证器信息的情况下实现了高成功率和图像质量。 AI

影响 这项研究突显了当前AI水印技术的潜在漏洞，可能影响内容真实性和来源追踪。

排序理由 该集群描述了一篇详细介绍攻击生成式AI模型水印新方法的创新研究论文。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的DRIFT攻击以98-100%的成功率移除扩散模型水印

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rui Bao, Zheng Gao, Xiaoyu Li, Xiaoyan Feng, Yang Song, Jiaojiao Jiang ·

    DRIFT：通过偏转生成轨迹来移除扩散水印

    arXiv:2609.08213v1 Announce Type: cross Abstract: Diffusion watermarking embeds verifiable signals into the generative process and commonly verifies them by recovering trajectory-dependent evidence, making the marks robust to conventional pixel-space distortions. Existing removal…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    DRIFT：通过偏转生成轨迹来移除扩散水印

    Diffusion watermarking embeds verifiable signals into the generative process and commonly verifies them by recovering trajectory-dependent evidence, making the marks robust to conventional pixel-space distortions. Existing removal attacks either regenerate along deterministic tra…

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