In the ever-expanding digital landscape, safeguarding sensitive information
remains paramount. This paper delves deep into digital protection, specifically
focusing on steganography. While prior research predominantly fixated on
individual bit decoding, we address this limitation by introducing ``message
accuracy'', a novel metric evaluating the entirety of decoded messages for a
more holistic evaluation. In addition, we propose an adaptive universal loss
tailored to enhance message accuracy, named Log-Sum-Exponential (LSE) loss,
thereby significantly improving the message accuracy of recent approaches.
Furthermore, we also introduce a new latent-aware encoding technique in our
framework named \Approach, harnessing pretrained Stable Diffusion for advanced
steganographic image generation, giving rise to a better trade-off between
image quality and message recovery. Throughout experimental results, we have
demonstrated the superior performance of the new LSE loss and latent-aware
encoding technique. This comprehensive approach marks a significant step in
evolving evaluation metrics, refining loss functions, and innovating image
concealment techniques, aiming for more robust and dependable information
protection.

这篇研究论文深入探讨数字保护，特别聚焦于信息隐藏技术。通过引入评估已解码信息的新颖度量标准 “信息准确性”，我们解决了以往研究局限于个别位解码的问题。此外，我们提出了一种适应性通用损失函数 Log-Sum-Exponential（LSE）来增强信息准确性，从而显著改进了最近方法的消息准确性。同时，我们还在我们的框架中引入了一种新的潜在感知编码技术 Approach，利用预训练稳定扩散技术生成先进的隐写图像，实现图像质量和信息恢复之间更好的权衡。通过实验结果，我们展示了新的 LSE 损失和潜在感知编码技术的卓越性能。这种全面的方法在进化评估指标、优化损失函数和创新图像隐藏技术方面迈出了重要一步，旨在实现更强大可靠的信息保护。