Abstract
The traditional steganalysis feature extraction method can be mainly divided into two steps. First, the residual image is calculated by convolution filtering, and then the co-occurrence matrix of residual image is calculated to obtain the final feature. Previous work calculates the residual image usually through a set of fixed high-pass filter or a manually designed residual sub-model, and does not utilize the consistency between pixels in the local area of the natural image. In this paper, we propose the Non-negative Matrix Factorization (NMF) based steganalysis feature extraction method for spatial image. Considering the number of pixels used by NMF for prediction and its positional relationship with the predicted pixels, a plurality of sets of residual sub-models for acquiring residual images are designed; and then, a new residual combination method is proposed, combining Local Binary Pattern (LBP), co-occurrence matrix and other statistical information, and the parameters in feature extraction is optimized. Finally, we compare the performance of the designed features with existing steganalysis features and analyze the validity of the designed features. In addition, we combine the existing artificially designed spatial steganalysis features with the designed features and analyze the validity and complementarity of each type of features, such as Spatial Rich Model (SRM) and Threshold LBP (TLBP).
This work was supported by the National Natural Science Foundation of China (NSFC) under the grant No. U1836102.
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Ge, H., Hu, D., Xu, H., Li, M., Zheng, S. (2020). New Steganalytic Features for Spatial Image Steganography Based on Non-negative Matrix Factorization. In: Wang, H., Zhao, X., Shi, Y., Kim, H., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2019. Lecture Notes in Computer Science(), vol 12022. Springer, Cham. https://doi.org/10.1007/978-3-030-43575-2_28
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DOI: https://doi.org/10.1007/978-3-030-43575-2_28
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