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A New Distortion Function Design for JPEG Steganography Using the Generalized Uniform Embedding Strategy

机译:基于广义均匀嵌入策略的JPEG隐写失真函数设计

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摘要

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time.
机译:如今,隐写术最流行的方法是最小化嵌入失真框架,该框架包括针对每个封面元素的可优化失真函数和一种将失真最小化的编码方法。随着综合格码的出现,失真功能在现代自适应图像隐写术中发挥着越来越重要的作用。在这封信中,提出了一种新的失真函数,称为JPEG隐写术,称为广义均匀嵌入失真(GUED)。拟议的GUED包括针对交流(AC)模式和离散余弦变换(DCT)块的新失真措施,它们以更通用的指数模型表示,旨在灵活分配嵌入数据,以最大程度地减少全局变化嵌入后量化DCT系数的统计信息。此外,根据有效载荷和品质因数,开发了经验规则来确定指数函数的参数。通过探索DCT和空间域的统计数据,表明提出的GUED与广义统一嵌入策略的目标更加一致,即保持DCT系数的相对变化与它们的变异系数成比例。大量实验表明,与最初的UERD相比,拟议的GUED在性能上有显着提高,并且在缩短计算时间方面优于最新的J-UNIWARD。

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