DBDH：一种用于不可见嵌入区域定位的双分支双头神经网络
DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization
AI总结：
本文提出双分支双头神经网络DBDH，通过高频纹理分支和上下文分支结合检测与分割头，实现图像中不可见嵌入区域的精确定位，实验证明其优于现有方法。
摘要：
在图像中嵌入不可见超链接或隐藏代码以替代二维码已成为近年来的热点。该技术需要在解码前首先在拍摄的照片中定位嵌入区域。现有训练模型以寻找不可见嵌入区域的方法难以获得准确定位结果，导致解码精度下降。这一局限主要源于CNN网络对低频信号敏感，而嵌入信号通常以高频形式存在。基于此，本文提出了一种专为不可见嵌入区域精确定位而设计的双分支双头（DBDH）神经网络。具体而言，DBDH使用一个包含62个高通滤波器的低级纹理分支来捕获由嵌入引起的高频信号。一个高级上下文分支用于提取嵌入区域与正常区域之间的判别性特征。DBDH采用检测头直接检测嵌入区域的四个顶点。此外，我们引入了一个额外的分割头，在训练期间分割嵌入区域的掩码。分割头为模型学习提供像素级监督，有助于更好地学习嵌入信号。基于两种最先进的不可见离线到在线消息传递方法，我们构建了两个数据集和增强策略，用于训练和测试定位模型。大量实验表明，所提出的DBDH相较于现有方法具有优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
