  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.
