Reversible data hiding using a transformer predictor and an adaptive embedding strategy

Abstract

In the field of reversible data hiding (RDH), designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects. In this paper, we propose a new RDH method, including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules. In the predictor part, we first design a transformer-based predictor. Then, we propose an image division method to divide the image into four parts, which can use more pixels as context. Compared with other predictors, the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones, making it more accurate in reducing the embedding distortion. In the embedding strategy part, we first propose a complexity measurement with pixels in the target blocks. Then, we develop an improved prediction error ordering rule. Finally, we provide an embedding strategy including multiple embedding rules for the first time. The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images, and experimental results show that the performance of our RDH method is leading the field.

摘要

在可逆信息隐藏(RDH)领域中, 设计高精度预测器以减少嵌入失真和开发有效的嵌入策略以最小化由嵌入信息引起的失真是提高RDH性能的两个关键方面。本文提出一种新的RDH方法, 包括基于transformer的预测器和具有多个嵌入规则的新嵌入策略。在预测器部分, 我们首先设计了一个基于transformer的预测器。然后, 提出一种图像分割方法, 将图像分成4部分, 可以使用更多的像素作为上下文。与其他预测器相比, 我们的预测器可以将用于预测的像素范围从相邻像素扩展到全局像素, 从而使其在减少嵌入失真方面更为准确。在嵌入策略部分, 我们首先提出了能够利用目标块中像素的复杂性度量。然后, 开发了一种改进的预测误差排序规则。最后, 我们首次提出一种包含多个嵌入规则的嵌入策略。本文中的RDH方法可以有效减少失真, 同时在提高隐藏图像的视觉质量方面提供令人满意的结果。实验结果表明, 本文中提出的RDH算法的性能处于领先地位。