Abstract
The convolutional neural network (CNN) based image steganalyzers have evolved remarkably over the past few years, and designing suitable CNN structures has been currently the fundamental method to improve the detection accuracy. However, CNN-based steganalyzer with the universal activation functions of the computer vision (CV) field barely achieves significant performance improvement. Therefore, a dedicated activation function is required to improve the detection performance for image steganalysis. In this paper, we propose a point-wise parametric rectified unit (PW-PReLU) which has different adaptively learnable parameters for each pixel of the negative inputs to facilitate the representation capacity of the activated feature maps. Then, in order to further boost the detection accuracy, the feature fusion is realized by the concatenation operation in the first layer. Based on the above components, an optimized CNN-based steganalyzer is proposed for spatial image steganalysis. The results of comparative experiments demonstrate that the proposed network can detect the state-of-the-art spatial steganographic algorithms with better performance than the previous steganalyzers on the 512 \(\times \) 512 BOSSbase_1.01 dataset and the resized 256 \(\times \) 256 union dataset of BOSSbase_1.01 and BOWS2.
This work was supported by the National Natural Science Foundation of China under Grant 61872368 and Grant 61802410.
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References
Fridrich, J., Filler, T.: Practical method for minimizing the embedding impact in steganography. In: SPIE, vol. 6505, pp. 650520-1–650520-15 (2007)
Pevný, T.F., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Proceedings of 12th Information Hiding Workshop, pp. 161–177 (2010)
Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: Proceedings of the International Workshop on Information Forensics and Security, pp. 234–239 (2012)
Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. IEEE Trans. Image Process. 2014(1), 1–13 (2017)
Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: Proceedings of the International Conference on Image Processing, pp. 3320–3328 (2014)
Li, B., Wang, M., Li, X., Tan, S., Huang, J.: A strategy of clustering modification directions in spatial image steganography. IEEE Trans. Inf. Forensic Secur. 10(9), 1905–1917 (2015)
Fridrich, J., Kodovský, J.: Rich model for steganalysis of digital images. IEEE Trans. Inf. Forensic Secur. 7(3), 868–882 (2012)
Fridrich, J., Holub, V.: Selection-channel-aware rich model for steganalysis of digital images. In: Proceedings of the IEEE International Workshop on Information Forensics and Security, pp. 48–53 (2014)
Tan, S., Li, B.: Stacked convolutional auto-encoders for steganalysis of digital images. In: Proceedings of Signal and Information Processing Association Annual Summit and Conference, pp. 1–4 (2014)
Qian, Y., Dong, J., Wang, W.: Deep learning for steganalysis via convolutional neural network. In: SPIE Media Watermarking, Security, and Forensics, vol. 9409 (2015)
Xu, G., Wu, H., Shi, Y.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)
Ye, J., Ni, Q., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensic Secur. 12(11), 2545–2557 (2017)
Li, B., Wei, W., Tan, S.: ReST-Net: diverse activation models and parallel subnets-based CNN for spatial image steganalysis. IEEE Signal Process. Lett. 25(5), 650–654 (2018)
Boroumand, M., Chen, M., Fridrich, J.: Deep residual network for steganalysis of digital images. IEEE Trans. Inf. Forensic Secur. https://ieeexplore.ieee.org/document/8470101
Xu, G., Wu, H., Shi, Y.: Ensemble of CNNs for steganalysis: an empirical study. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 103–107 (2016)
Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2014)
He, K., Zhang, X., Sun, J.: Deep residual learning learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Bas, P., Filler, T., Pevný, T.: Break our steganographic system: the ins and outs of organizing boss. In: Proceedings of the Information Hiding Workshop, pp. 225–230 (2011)
Bas, P., Furon, T.: BOWS-2 (2007). http://bows2.ec-lille.fr
LeCun, Y., Boser, B., Jackel, L.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, vol. 9, pp. 249–256 (2010)
Glorot, X., Bordes, A., Bengio, Y.: Deep spares rectifier neural networks. In: Proceedings of the IEEE International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning, vol. 28 (2013)
Clevert, D., Unterthiner, T., Hochreiter, S.: Fast and accurate deep neural network learning by exponential linear units (ELUs). In: Proceedings of the International Conference for Learning Representations (2016)
Lin, M., Chen, Q., Yan, S.: Network in network. In: Proceedings of the International Conference for Learning Representations (2014). arXiv:1312.4400
Ioffe, S., Szegedy, C.: Batch Normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference on Machine Learning (2015). arXiv:1312.4400
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th International Conference on Machine Learning, pp. 41–48 (2009)
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Xue, Ym., Peng, Wl., Wang, Y., Wen, J., Zhong, P. (2020). Optimized CNN with Point-Wise Parametric Rectified Linear Unit for Spatial Image Steganalysis. 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_3
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