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

Robust Image Watermarking based on Cross-Attention and Invariant Domain  Learning

Denoising Diffusion Models (DDM) are emerging as the cutting-edge technology
in the realm of deep generative modeling, challenging the dominance of
Generative Adversarial Networks. However, effectively exploring the latent
space's semantics and identifying compelling trajectories for manipulating and
editing important attributes of the generated samples remains challenging,
primarily due to the high-dimensional nature of the latent space. In this
study, we specifically concentrate on face rotation, which is known to be one
of the most intricate editing operations. By leveraging a recent embedding
technique for Denoising Diffusion Implicit Models (DDIM), we achieve, in many
cases, noteworthy manipulations encompassing a wide rotation angle of $\pm
30^o$, preserving the distinct characteristics of the individual. Our
methodology exploits the computation of trajectories approximating clouds of
latent representations of dataset samples with different yaw rotations through
linear regression. Specific trajectories are obtained by restricting the
analysis to subsets of data sharing significant attributes with the source
image. One of these attributes is the light provenance: a byproduct of our
research is a labeling of CelebA, categorizing images into three major groups
based on the illumination direction: left, center, and right.

利用噪声扩散模型，通过嵌入技术在人脸旋转方面实现了显著的操作和编辑，保留了个体的显著特征。