AI 中文总结
针对现有反转水印在旋转等复合攻击下鲁棒性不足的问题，提出无训练的AnchorMark，利用隐空间旋转同步属性嵌入同步锚点，提升旋转及复合攻击下的比特准确率且对图像质量影响小。
AI 中文摘要
基于反转的水印将水印载荷直接嵌入生成过程，无需单独的事后图像域嵌入阶段，同时保留合成图像的原生视觉保真度。然而，现有方法仍易受复合有损后处理攻击，尤其在涉及旋转时，因为旋转会破坏隐空间解码所需的空间对应关系。为克服这一局限，我们提出AnchorMark，一种无训练的鲁棒反转水印。我们发现了隐空间中名为旋转同步的属性：图像域旋转与恢复的初始隐空间中的对应旋转具有相同角度。基于此属性，AnchorMark在初始隐空间的中心区域嵌入一个同步锚点，使得提取过程中能够准确估计和校正旋转角度。实验表明，AnchorMark在旋转及复合攻击下显著提升了比特准确率，且对图像质量的影响有限。
英文摘要
Inversion-based watermarking embeds watermark payloads directly into the generative process, avoiding a separate post-hoc image-domain embedding stage while preserving the native visual fidelity of synthesized images. However, existing methods remain vulnerable to compound lossy post-processing, particularly when rotation is involved, as it disrupts the spatial correspondence required for latent-space decoding. To overcome this limitation, we introduce AnchorMark, a training-free, robust inversion-based watermarking. We uncover a latent-space property termed Rotation Synchrony: image-domain rotations and their counterparts in the recovered initial latent share the same angle. Building on this property, AnchorMark embeds a synchronization anchor in the central region of the initial latent, enabling accurate estimation and correction of the rotation angle during extraction. Experiments show that AnchorMark substantially improves bit accuracy under rotation and combined attacks, with limited impact on image quality.
