用生成模型潜空间优化选图，提升隐写藏密效果
Neural Cover Selection for Image Steganography
- 在预训练生成模型潜空间中搜索最优载体图
- 藏密恢复率和图像质量显著优于传统方法
- 发现低方差像素是藏密主要区域，理论契合水填算法
隐写中选择最佳载体图像（即载体选择）对有效信息隐藏至关重要。传统方法通常采用穷举搜索，筛选符合特定感知或复杂度指标的图像，但这些指标与实际藏密效果的关系不明确，常导致不佳结果。受生成模型进展启发，我们提出一种新型载体选择框架：在预训练生成模型的潜空间中进行优化，以识别最适合作为载体的图像，区别于传统穷举法。该方法在消息恢复率和图像质量方面表现显著提升。我们还对生成的载体图像进行了信息论分析，发现信息隐藏主要发生在低方差像素上，这符合并行高斯信道中的水填算法原理。代码已公开于：https://github.com/karlchahine/Neural-Cover-Selection-for-Image-Steganography。
原文摘要 · Abstract (English)
In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment. Traditional methods have typically employed exhaustive searches to identify images that conform to specific perceptual or complexity metrics. However, the relationship between these metrics and the actual message hiding efficacy of an image is unclear, often yielding less-than-ideal steganographic outcomes. Inspired by recent advancements in generative models, we introduce a novel cover selection framework, which involves optimizing within the latent space of pretrained generative models to identify the most suitable cover images, distinguishing itself from traditional exhaustive search methods. Our method shows significant advantages in message recovery and image quality. We also conduct an information-theoretic analysis of the generated cover images, revealing that message hiding predominantly occurs in low-variance pixels, reflecting the waterfilling algorithm's principles in parallel Gaussian channels. Our code can be found at: https://github.com/karlchahine/Neural-Cover-Selection-for-Image-Steganography.
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