This paper presents a new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques, i.e., watermarking and steganography. The novelty claim is the use of evaluation matrix (EM) for performance evaluation of data hiding techniques; however, one major problem with performance evaluation of data hiding techniques is to find reasonable thresholds for performance metrics and the trade-off among them in different data hiding applications. Two experiments are conducted. The first experiment included LSB techniques (eight approaches) based on different payload results and the noise gate approach; a total of nine approaches were used. Five audio samples with different audio styles are tested using each of the nine approaches and considering three evaluation criteria, namely, complexity, payload, and quality, to generate watermarked samples. The second experiment involves the use of various decision-making techniques simple additive weighting (SAW), multiplicative exponential weighting (MEW), hierarchical adaptive weighting (HAW), technique for order of preference by similarity to ideal solution (TOPSIS), weighted sum model (WSM) and weighted product method (WPM) to benchmark the results of the first experiment. Mean, standard deviation (STD), and paired sample t-test are then performed to compare the correlations among different techniques on the basis of ranking results. The findings are as follows: (1) A statistically significant difference is observed among the ranking results of each multi-criteria decision-making (MCDM) technique, (2) TOPSIS-Euclidean is the best technique to solve the benchmarking problem among digital watermarking techniques. (3) Among the decision-making techniques, WSM has the lowest rank in terms of solving the benchmarking problem. (4) Under different circumstances, the noise gate watermarking approach performs better than LSB algorithms.

本文提出了一种基于多维评估和基准测试的数据隐藏技术（即水印和隐写术）的新方法。新颖性主张是使用评估矩阵（EM）来评估数据隐藏技术的性能；然而，数据隐藏技术性能评估的一个主要问题是找到合理的性能指标阈值以及在不同数据隐藏应用中它们之间的权衡。进行了两个实验。第一个实验包括基于不同有效负载结果和噪声门方法的LSB技术（八种方法）；总共使用了九种方法。使用九种方法中的每一种来测试具有不同音频风格的五个音频样本，并考虑三个评估标准，即复杂性、有效负载和质量，以生成带水印的样本。第二个实验涉及使用各种决策技术：简单加法加权（SAW）、乘法指数加权（MEW）、分层自适应加权（HAW）、通过与理想解相似度的偏好顺序技术（TOPSIS）、加权和模型（WSM）和加权乘积法（WPM）来对第一个实验的结果进行基准测试。然后执行平均值、标准差 (STD) 和配对样本t检验，以在排序结果的基础上比较不同技术之间的相关性。研究结果如下：（1）每种多标准决策（MCDM）技术的排名结果之间观察到统计上显着的差异，（2）TOPSIS-Euclidean是解决数字水印基准问题的最佳技术技术。 (3) 在决策技术中，WSM 在解决基准问题方面排名最低。 (4)在不同情况下，噪声门水印方法的性能优于LSB算法。