Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach which attempts to perform steganalysis tasks in effectively. However, this research method does not concentrate the noises present in the images. It might increase the computational overhead where the error cost adjustment would require more iteration. This is resolved in the proposed research technique by introducing the novel research method called Non-Gaussian Noise Aware Auto Encoder Convolutional Neural Network (NGN-AEDNN). Classification technique provides a more flexible way for steganalysis where the multiple features present in the environment would lead to an inaccurate prediction rate. Here, learning accuracy is improved by introducing noise removal techniques before performing a learning task. Non-Gaussian Noise Removal technique is utilized to remove the noises before learning. Also, Gaussian noise removal is applied at every iteration of the neural network to adjust the error rate without the involvement of noisy features. This proposed work can ensure efficient steganalysis by accurate learning task. Matlab has been employed to implement the method by performing simulations from which it is proved that the proposed research technique NGN-AEDNN can ensure the efficient steganalysis outcome with the reduced computational overhead when compared with the existing methods.

中文翻译：

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使用卷积自动编码器网络进行高效隐写分析，以确保原始图像质量

隐写分析是分析和预测图像中隐藏信息的存在的过程。隐写分析对于预测接收到的图像是否包含有用信息最有用。然而，预测图像中的隐藏信息更加困难，这在计算上是困难的。在现有的研究方法中，这是通过引入深度学习方法来解决的，该方法试图有效地执行隐写分析任务。但是，该研究方法并未集中图像中存在的噪声。在错误成本调整将需要更多迭代的情况下，这可能会增加计算开销。通过引入称为“非高斯噪声感知自动编码器卷积神经网络”（NGN-AEDNN）的新颖研究方法，可以解决该问题。分类技术为隐写分析提供了一种更灵活的方法，其中环境中存在的多个特征将导致不正确的预测率。在此，通过在执行学习任务之前引入噪声消除技术来提高学习准确性。非高斯噪声去除技术用于在学习之前去除噪声。同样，在神经网络的每次迭代中都应用高斯噪声去除，以在不涉及噪声特征的情况下调整错误率。这项拟议的工作可以通过准确的学习任务来确保有效的隐写分析。