Image watermarking involves embedding and extracting watermarks within a cover image, with deep learning approaches emerging to bolster generalization and robustness. Predominantly, current methods employ convolution and concatenation for watermark embedding, while also integrating conceivable augmentation in the training process. This paper explores a robust image watermarking methodology by harnessing cross-attention and invariant domain learning, marking two novel, significant advancements. First, we design a watermark embedding technique utilizing a multi-head cross attention mechanism, enabling information exchange between the cover image and watermark to identify semantically suitable embedding locations. Second, we advocate for learning an invariant domain representation that encapsulates both semantic and noise-invariant information concerning the watermark, shedding light on promising avenues for enhancing image watermarking techniques.

通过跨注意力和不变域学习，本文探索了一种强大的图像水印方法。首先，我们设计了一种利用多头交叉注意力机制的水印嵌入技术，实现了封面图像和水印之间的信息交流，以识别语义上合适的嵌入位置。其次，我们倡导学习一个包含语义和噪声不变信息的不变域表示，为提升图像水印技术指明了前景。

基于交叉注意力和不变领域学习的鲁棒图像水印技术