AI Article Synopsis
- The study presents a new steganalysis method for JPEG images that utilizes convolutional neural networks (CNNs) to address challenges in high-dimensional data.
- The proposed algorithm enhances the original rich model by incorporating various sizes of discrete cosine transform (DCT) basis functions to extract diverse detection features.
- Experimental findings indicate that CNNs can classify effectively with fewer training samples compared to traditional ensemble classifiers, leading to improved overall performance.
Article Abstract
The best traditional steganalysis methods aiming at adaptive steganography are the combination of rich models and ensemble classifier. In this study, a new steganalysis method for JPEG images based on convolutional neural networks is proposed to solve the high dimension problem in steganalysis from another aspect. On the basis of the original rich model, the algorithm adds different sizes of discrete cosine transform (DCT) basis functions to extract different detection features. Different features are combined at the fully connected layer through inputting 2-D feature values to the neural network convolutional layer for predictive classification. Experimental results show that convolutional neural networks as classifiers do not require a large number of training samples, and the final classification performance is better than that of the original ensemble classifier.
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| http://dx.doi.org/10.3934/mbe.2019201 | DOI Listing |
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