Traditional steganographic techniques have often relied on manually crafted attributes related to image residuals. These methods demand a significant level of expertise and face challenges in integrating diverse image residual characteristics. In this paper, we introduce an innovative deep learning-based methodology that seamlessly integrates image residuals, residual distances, and image local variance to autonomously learn embedding probabilities. Our framework includes an embedding probability generator and three pivotal guiding components: Residual guidance strives to facilitate embedding in complex-textured areas. Residual distance guidance aims to minimize the residual differences between cover and stego images. Local variance guidance effectively safeguards against modifications in regions characterized by uncomplicated or uniform textures. The three components collectively guide the learning process, enhancing the security performance. Comprehensive experimental findings underscore the superiority of our approach when compared to traditional steganographic methods and randomly initialized ReLOAD in the spatial domain.

翻译：传统的隐写技术通常依赖于人工设计的与图像残差相关的属性。这些方法需要较高的专业知识水平，并且在整合多种图像残差特征方面面临挑战。本文提出了一种基于深度学习的创新方法，能够无缝集成图像残差、残差距离和图像局部方差，以自主学习嵌入概率。我们的框架包括一个嵌入概率生成器和三个关键引导组件：残差引导旨在促进在复杂纹理区域进行嵌入；残差距离引导旨在最小化载体图像与隐写图像之间的残差差异；局部方差引导则有效地防止对简单或均匀纹理区域的修改。这三个组件共同引导学习过程，从而提升安全性能。全面的实验结果表明，与传统的隐写方法以及空间域中随机初始化的ReLOAD相比，我们的方法具有显著优势。