Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.

本文提出了一种基于Dual-Branch Dual-Head（DBDH）神经网络的不可见嵌入区域精确定位技术，利用低级纹理分支和高级上下文分支来捕捉高频信号以及提取嵌入和正常区域的区分特征，通过检测头直接检测嵌入区域的四个顶点，并引入额外的分割头用于训练期间提供像素级别监督，以提高模型对嵌入信号的学习能力。实验结果表明，所提出的DBDH方法优于现有方法。

DBDH: 一种用于隐形嵌入区域定位的双支路双头神经网络