Camera recapture introduces complex optical degradations, such as perspective warping, illumination shifts, and Moiré interference, that remain challenging for deep watermarking systems. We present TIACam, a text-anchored invariant feature learning framework with auto-augmentation for camera-robust zero-watermarking. The method integrates three key innovations: (1) a learnable auto-augmentor that discovers camera-like distortions through differentiable geometric, photometric, and Moiré operators; (2) a text-anchored invariant feature learner that enforces semantic consistency via cross-modal adversarial alignment between image and text; and (3) a zero-watermarking head that binds binary messages in the invariant feature space without modifying image pixels. This unified formulation jointly optimizes invariance, semantic alignment, and watermark recoverability. Extensive experiments on both synthetic and real-world camera captures demonstrate that TIACam achieves state-of-the-art feature stability and watermark extraction accuracy, establishing a principled bridge between multimodal invariance learning and physically robust zero-watermarking.

翻译：相机重摄会引入复杂的光学退化，如透视扭曲、光照偏移和莫尔条纹干扰，这对深度水印系统仍构成挑战。本文提出TIACam，一种面向相机鲁棒零水印的文本锚定不变特征学习框架，具备自动增强能力。该方法融合了三大核心创新：(1) 可学习的自动增强器，通过可微的几何、光度与莫尔算子来发现类相机失真；(2) 文本锚定不变特征学习器，通过图像与文本间的跨模态对抗对齐来强化语义一致性；(3) 零水印头部，在不修改图像像素的前提下将二进制信息绑定于不变特征空间。这一统一框架联合优化了不变性、语义对齐与水印可恢复性。在合成与真实世界相机采集数据上的大量实验表明，TIACam实现了最先进的特征稳定性与水印提取准确率，为多模态不变性学习与物理鲁棒的零水印之间建立了原理性桥梁。