自适应最高有效位预测（adaptive most significant bit prediction,AMP）是当前实现加密域可逆信息隐藏的一种重要算法。通过比较分块内首像素与其余像素，提取出公共最高有效位并用于像素预测，可节省空间嵌入秘密信息。然而，分块内首像素与其他像素差异较大，会制约嵌入容量。为解决这个问题，本文将分块内嵌入数据时没有变化的像素聚集，构建为虚拟像素块。通过对虚拟像素块再次执行AMP算法，实现对当前方案嵌入容量的提升。为进一步增加虚拟像素块的数量，本文提出一种填充策略。以2×2的像素块为例，在分块嵌入容量足够大时，通过填充固定比特位增加像素相关性，使得嵌入过程中前两个像素保持不变，这样全部可用来构建虚拟块。因为填充策略构建T字段并提出新的32 bits像素结构，该结构能够提升像素相关性从而提升嵌入容量。通过在真实世界数据集上开展实验，结果表明本文所提算法相比当前的主流AMP算法，能够在保证可逆的前提下大幅提高嵌入容量。
Adaptive most significant bit prediction (AMP) is an important technique to achieve reversible data hiding in encrypted images. AMP typically predicts pixel values by comparing the first pixel within a block with the remaining ones, extracting the longest common most significant bits to create space for embedding secret information. However, the significant difference between the first pixel and others can limit the embedding capacity. To solve this problem, this paper aggregates the unchanged pixels after data embedding within blocks and constructs a virtual pixel block. By applying the AMP algorithm again to the virtual pixel block, the proposed scheme enhances the embedding capacity. In order to further increase the number of virtual pixel blocks, this paper proposes a filling strategy. Taking a 2×2 pixel block as an example, when the block’s embedding capacity is large enough, the pixel correlation is increased by filling fixed bits. This ensures the first two pixels remain unchanged during embedding and can all be used to construct a virtual block. Because of the T field constructed by the filling strategy and the new 32 bits pixel structure proposed, pixel correlation is enhanced and embedding capacity is increased as a result. Experimental results on real-world datasets demonstrate that the proposed algorithm significantly outperforms existing AMP algorithms in terms of embedding capacity while maintaining data reversibility.
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