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A Steganalysis framework based on CNN using the filter subset selection method

机译：使用滤波器子集选择方法基于CNN的麻木分析框架

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摘要

The steganalysis method based on Convolutional Neural Network (CNN) has attracted wide attention in the field of steganalysis. This method typically uses high-pass or derivative filters to pre-process images. Multiple filters can be used to improve the detection accuracy. However, the use of multiple filters may generate redundant residual images and redundant features. Redundant features not only consume computing resources and time but also cause model over-fitting, thus compromising the detection accuracy of the model. Therefore, we proposed a filter subset selection method to develop a well-designed pre-processing layer for CNN-based steganalysis framework. This method discarded many high-pass and derivative filters according to the mechanism of convolution operation and the correlations between pixels. Structural Similarity (SSIM) was used to calculate the similarity between the filtered residual images and arrange them in ascending order. Finally, based on the arranged filters, a series of experiments were conducted to determine the optimal filter subset and the optimal CNN-based steganalysis framework. The experimental results indicate that the proposed method not only guarantees detection accuracy but also improves the training efficiency of the model. Therefore, this method offers an optimized trade-off between computational complexity and detection accuracy.
机译：基于卷积神经网络（CNN）的隐分方法引起了隐藏的领域。该方法通常使用高通或衍生滤波器来预处理图像。多个过滤器可用于提高检测精度。然而，使用多个滤波器可能会产生冗余的剩余图像和冗余特征。冗余功能不仅消耗计算资源和时间，而且也会导致模型过度拟合，从而损害模型的检测精度。因此，我们提出了一种过滤器子集选择方法，用于开发用于基于CNN的隐分框架的精心设计的预处理层。该方法根据卷积操作的机制和像素之间的相关性丢弃许多高通和衍生滤波器。使用结构相似性（SSIM）来计算滤波后的残差之间的相似性，并按升序排列。最后，基于布置的滤波器，进行了一系列实验以确定最佳过滤器子集和最佳的基于CNN的隐分框架。实验结果表明，该方法不仅保证了检测准确性，还可以提高模型的培训效率。因此，该方法在计算复杂性和检测准确性之间提供优化的权衡。

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