Image steganalysis has witnessed significant development but still encounters challenges in detection speed and accuracy. Based on this consideration, this paper proposes a simple yet efficient dominant feature selection method. First, a separability measurement is designed utilizing the principles of “intraclass aggregation and interclass dispersion” and “maximum interclass disparity”, by which the contribution of each feature is better evaluated. Second, a separability measurement is presented considering the “holistic interclass disparity”, and thus dominant features are directly determined. Moreover, a compensation strategy is proposed to reduce the possibility of missing dominant features, thereby further enhancing the accuracy of the selected features. The effectiveness of the proposed method is empirically verified. Extensive experiments are conducted on the BOSSbase 1.01 and ALASKA2 datasets. The results show that, compared to some state-of-the-art works, the proposed method achieves better performance in terms of computational cost, feature dimension, and detection accuracy. In particular, the computational cost of the proposed method is extremely low.

中文翻译：

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快速主导特征选择和补偿以实现高效图像隐写分析

图像隐写分析已经取得了长足的发展，但在检测速度和准确性方面仍然面临挑战。基于这种考虑，本文提出了一种简单而高效的主导特征选择方法。首先，利用“类内聚合、类间分散”和“类间最大差异”的原则设计可分离性度量，从而更好地评估每个特征的贡献。其次，考虑“整体类间差异”，提出可分离性测量，从而直接确定主导特征。此外，还提出了一种补偿策略，以减少丢失主导特征的可能性，从而进一步提高所选特征的准确性。所提出方法的有效性得到了实证验证。在 BOSSbase 1.01 和 ALASKA2 数据集上进行了大量实验。结果表明，与一些最先进的工作相比，所提出的方法在计算成本、特征维度和检测精度方面取得了更好的性能。特别是，所提出方法的计算成本极低。