The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Structural Design of Convolutional Neural Networks for Steganalysis
Abstract
Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144
Cited by
Deep residual learning for image steganalysis
Wu, S., Zhong, S., & Liu, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4440-4 · Cited by 442
Zhang, R., Zhu, F., Liu, J., & Liu, G. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2936913 · Cited by 420
A New Payload Partition Strategy in Color Image Steganography
Liao, X., Yu, Y., Li, B., Li, Z., & Qin, Z. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2896270 · Cited by 328
Yedroudj-Net: An Efficient CNN for Spatial Steganalysis
Yedroudj, M., Comby, F., & Chaumont, M. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461438 · Cited by 303
CNN-Based Adversarial Embedding for Image Steganography
Tang, W., Li, B., Tan, S., Barni, M., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2891237 · Cited by 292
Metadata
- Authors
- Xu, G., Wu, H.-Z., & Shi, Y.-Q.
- Venue
- IEEE Signal Processing Letters
- Published
- 2016-05
- Volume
- 23
- Issue
- 5
- Pages
- 708-712
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2322622188
- Cited by
- 840
- References
- 7
- FWCI
- 39.93
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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