论文标题
Stegcolnet：基于合奏色彩空间方法的stegansys
StegColNet: Steganalysis based on an ensemble colorspace approach
论文作者
论文摘要
图像隐志是指隐藏图像中信息的过程。切解分析是检测地理图像的过程。我们介绍了一种使用集合颜色空间模型来获取加权串联特征激活图的切解方法。串联地图有助于获得每个颜色空间的某些特征。我们使用Levy-Flight灰狼优化策略来减少地图中选择的功能数量。然后，我们使用这些功能将图像分类为两个类之一：给定图像是否存储了秘密信息。已经在从Bossbase数据集提取的大规模数据集上进行了广泛的实验。另外，我们表明该模型可以转移到不同的数据集中，并在数据集的混合物上执行广泛的实验。我们的结果表明，所提出的方法的表现超过了最新的最新技术深度学习方法，平均每通道0.2位（BPC）平均为2.32％，而0.4 bpc的平均状态平均为1.87％。
Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. We introduce a steganalysis approach that uses an ensemble color space model to obtain a weighted concatenated feature activation map. The concatenated map helps to obtain certain features explicit to each color space. We use a levy-flight grey wolf optimization strategy to reduce the number of features selected in the map. We then use these features to classify the image into one of two classes: whether the given image has secret information stored or not. Extensive experiments have been done on a large scale dataset extracted from the Bossbase dataset. Also, we show that the model can be transferred to different datasets and perform extensive experiments on a mixture of datasets. Our results show that the proposed approach outperforms the recent state of the art deep learning steganalytical approaches by 2.32 percent on average for 0.2 bits per channel (bpc) and 1.87 percent on average for 0.4 bpc.
