可逆隐写中的可预测性
On the predictability in reversible steganography
摘要：
人工神经网络推进了可逆隐写术的前沿。神经网络的核心优势是能够对令人眼花缭乱的各种数据进行准确预测。残差调制是目前公认的最先进的数字图像可逆隐写算法。该算法的核心是预测分析，其中给定一些像素上下文信息来预测像素强度。这个任务可以被看作是一个低层次的视觉问题，因此神经网络解决类似的一类问题可以部署。在现有技术之上，本文研究了基于监督和无监督学习框架的像素强度的可预测性。可预测性分析使自适应数据嵌入成为可能，这反过来又会在容量和不可感知性之间实现更好的权衡。虽然传统方法通过局部图像模式的统计来估计可预测性，但基于学习的框架进一步考虑指定预测器可以做出正确预测的程度。不仅要考虑图像模式，而且要考虑使用中的预测器。实验结果表明，将基于学习的可预测性分析器纳入可逆隐写系统可以显着提高隐写性能。
英文摘要：
Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most advanced reversible steganographic algorithm for digital images. The pivot of this algorithm is predictive analytics in which pixel intensities are predicted given some pixel-wise contextual information. This task can be perceived as a low-level vision problem and hence neural networks for addressing a similar class of problems can be deployed. On top of the prior art, this paper investigates predictability of pixel intensities based on supervised and unsupervised learning frameworks. Predictability analysis enables adaptive data embedding, which in turn leads to a better trade-off between capacity and imperceptibility. While conventional methods estimate predictability by the statistics of local image patterns, learning-based frameworks consider further the degree to which correct predictions can be made by a designated predictor. Not only should the image patterns be taken into account but also the predictor in use. Experimental results show that steganographic performance can be significantly improved by incorporating the learning-based predictability analysers into a reversible steganographic system.
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