  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.
