DOI QR코드

DOI QR Code

Experimental Comparison of CNN-based Steganalysis Methods with Structural Differences

구조적인 차이를 가지는 CNN 기반의 스테그아날리시스 방법의 실험적 비교

  • Received : 2018.12.31
  • Accepted : 2019.02.18
  • Published : 2019.03.30

Abstract

Image steganalysis is an algorithm that classifies input images into stego images with steganography methods and cover images without steganography methods. Previously, handcrafted feature-based steganalysis methods have been mainly studied. However, CNN-based objects recognition has achieved great successes and CNN-based steganalysis is actively studied recently. Unlike object recognition, CNN-based steganalysis requires preprocessing filters to discriminate the subtle difference between cover images from stego images. Therefore, CNN-based steganalysis studies have focused on developing effective preprocessing filters as well as network structures. In this paper, we compare previous studies in same experimental conditions, and based on the results, we analy ze the performance variation caused by the differences in preprocessing filter and network structure.

영상 스테그아날리시스는 입력 영상을 스테가노그래피 알고리즘이 적용된 스테고 영상과 스테가노그래피 알고리즘이 적용되지 않은 커버 영상으로 분류하는 알고리즘이다. 기존에는 주로 수제 특징 기반의 스테그아날리시스를 연구하였다. 하지만 CNN 기반의 물체 인식이 큰 성과를 이루면서 최근 CNN 기반의 스테그아날리시스가 활발히 연구되고 있다. CNN 기반의 스테그아날리시스는 물체 인식과는 달리 커버 영상과 스테고 영상의 미세한 차이를 식별하기 위해서 전처리 필터를 필요로 한다. 그러므로, CNN 기반의 스테그아날리시스 연구들은 효과적인 전처리 필터와 네트워크 구조를 개발하는 데 초점을 두고 있다. 본 논문에서는 동일한 실험 조건에서 기존 연구들을 비교하고, 그 결과를 기반으로 전처리 필터와 네트워크 구조적인 차이에 의한 성능 변화를 분석한다.

Keywords

BSGHC3_2019_v24n2_315_f0001.png 이미지

그림 1. BOSSBase 데이터 셋의 1,000장의 커버 영상과 1LSB(bpp = 0.4), S-UNIWARD(bpp = 0.4)에 의한 스테고 영상의 이웃 픽셀 사이의 값 차이 분포 Fig. 1. Histogram of neighboring pixel value differences of 1,000 cover images in BOSSBase data sets and their stego images made by 1LSB(bpp = 0.4) and S-UNIWARD(bpp = 0.4)

BSGHC3_2019_v24n2_315_f0005.png 이미지

그림 5. ABS와 BN 적용 전/후 히스토그램. (a) 입력, (b) ABS 적용 후, BN 적용 후 Fig. 5. Histogram before/after applying ABS and BN. (a) input, (b) after ABS, (c) after BN

BSGHC3_2019_v24n2_315_f0006.png 이미지

그림 6. WOW와 S-UNIWARD에 사용된 16 × 16 크기의 3가지 필터 Fig. 6. Three 16 × 16 filters used in WOW and S-UNIWARD

BSGHC3_2019_v24n2_315_f0007.png 이미지

그림 7. S-UNIWARD와 WOW에 의한 비밀 정보 삽입 영역. (a) 커버 영상, (b) S-UNIWARD, (c) WOW Fig. 7. Secret information insertion regions by S-UNIWARD and WOW. (a) Cover image, (b) S-UNIWARD, (c) WOW

BSGHC3_2019_v24n2_315_f0009.png 이미지

그림 9. DAM 구조 Fig. 9. DAM structure

BSGHC3_2019_v24n2_315_f0010.png 이미지

그림 10. 성능 분석에 사용된 CNN 기반의 스테그아날리시스 방법의 네트워크 구조. (a) [2]의 방법, (b) [3]의 방법, (c) [4]의 방법, (d) [5]의 방법, (e) [6]의 방법 Fig. 10. Network structures of CNN-based steganalysis methods used in performance analysis. (a) Method of [2], (b) method of [3], (c) method of [4], (d) method of [5], (e) method of [6]

BSGHC3_2019_v24n2_315_f0011.png 이미지

그림 2. 1차원 입력을 가지는 신경망의 계층. (a) 완전 연결 계층, (b) 합성곱 계층 Fig. 2. Layers in 1D-input neural network. (a) Fully connected layer, (b) convolutional layer

BSGHC3_2019_v24n2_315_f0012.png 이미지

그림 3. CNN 파라미터. 서로 다른 패딩 크기, 필터의 크기, 스트라이드, 필터 채널을 가지는 두 CNN, 각각 A = {0, 3, 1, 2}, B = {2, 5, 2, 1} Fig. 3. CNN parameters, Two CNNs with different padding sizes, filter sizes, stride, and filter channels, A = {0, 3, 1, 2}, B = {2, 5, 2, 1}

BSGHC3_2019_v24n2_315_f0013.png 이미지

그림 4. Xu-Net[2]에서 제안한 CNN Fig. 4. CNN proposed in Xu-net[2]

BSGHC3_2019_v24n2_315_f0014.png 이미지

그림 8. 4가지 활성화 함수. (a) TLU, (b) ReLU, (c) tanH, (d) sigmoid Fig. 8. 4 activation functions. (a) TLU, (b) ReLU, (c) tanH, (d) sigmoid

표 1. S-UNIWARD 알고리즘 분류 결과 Table 1. Classification results for S-UNIWARD

BSGHC3_2019_v24n2_315_t0001.png 이미지

표 2. WOW 알고리즘 분류 결과 Table 2. Classification results for WOW

BSGHC3_2019_v24n2_315_t0002.png 이미지

표 3. 실험에 사용된 각 CNN의 하이퍼파라미터 Table 3. Hyperparameters of CNNs used in experiments

BSGHC3_2019_v24n2_315_t0003.png 이미지

References

  1. L. Pibre, J. Pasquet, D. Ienco and M. Chaumont, "Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch," Society for Imaging Science and Technology, pp.1-11, 2016.
  2. G. Xu and H. Wu, "Structural design of convolutional neural networks for steganalysis," IEEE Signal Processing Letters, Vol.23, No.5, pp.708-712, 2016. https://doi.org/10.1109/LSP.2016.2548421
  3. Y. Yuan, W. Lu, B. Feng and J. Weng, "Steganalysis with CNN using multi-channels filtered residuals," ICCCS 2017, pp.110-120, 2017.
  4. J. Ye, J. Ni and Y. Yi, "Deep learning hierarchical representations for image steganalysis," IEEE Transactions on Information Forensics and Security, Vol.12, No.11, pp.2545-2557, 2017. https://doi.org/10.1109/TIFS.2017.2710946
  5. M. Yedroudj, F. Comby and M. Chaumont, "Yedroudj-Net: an efficient CNN for spatial steganalysis," ICASSP 2018, pp.15-20, 2018.
  6. B. Li, W. Wei, A. Ferreira and S. Tan, "ReST-Net: diverse activation modules and parallel subnets-based CNN for spatial image steganalysis," IEEE Signal Processing Letters, Vol.25, No.5, pp.650-654, 2018. https://doi.org/10.1109/LSP.2018.2816569
  7. J. Yang, K. Liu, X. Kang, E. Wong and Y. Shi, "Steganalysis based on awareness of selection-channel and deep learning," IWDW 2017, pp.263-272, 2017.
  8. D. Neeta and K. Snehal, "Implementation of LSB steganography and it s evaluation for various bits," 1st International Conference on Digital Information Management, pp.173-178, 2006.
  9. D. Wu and W. Tsai, "A steganographic method for images by pixel- value differencing," Pattern Recognition Letters, Vol.24, pp.1613-1626, 2003. https://doi.org/10.1016/S0167-8655(02)00402-6
  10. K. Chang, C. Chang, P. S. Huang and T. Tu, "A novel image steganographic method using tri-way pixel-value differencing," Journal of Multimedia, Vol.3, No.2, pp.37-44, 2008.
  11. C. Balasubramanian, S. Selvakumar and S. Geetha, "High payload image steganography with reduced distortion using octonary pixel pairing scheme," Multimed Tools Appl., Vol.73, pp.2223-2245, 2014. https://doi.org/10.1007/s11042-013-1640-4
  12. G. Gul and F. Kurugollu, "A new methodology in steganalysis: breaking highly undetectable steganography (HUGO)," International Workshop on Information Hiding, pp.71-84, 2011.
  13. V. Holub and J. Fridrich, "Designing steganographic distortion using directional filters," International Workshop on Information Forensics and Security, 2012.
  14. V. Holub, J. Fridrich and T. Denemark, "Universal distortion function for stegangography in an arbitrary domain," EURASIP Journal of Information Security, 2014.
  15. G. Cancelli, G. Doerr, I. J. Cox and M. Barni, "Detection of $\pm$1 LSB steganography based on the amplitude of histogram local extrema," ICIP, pp.1288-1291, 2008.
  16. T. Pevny, P. Bas and J. Fridrich, "Steganalysis by subtractive pixel adja cency matrix," IEEE Transactions on Information Forensics and Security, Vol.5, no.2, pp.215-224, June 2010. https://doi.org/10.1109/TIFS.2010.2045842
  17. J. Fridrich and J. Kodovsky, "Rich models for steganalysis of digital im ages," IEEE Transactions on Information Forensics and Security, Vol. 7, No.3, pp.868-882, June 2012. https://doi.org/10.1109/TIFS.2012.2190402
  18. R. Haeb-Umbach and H. Ney, "Linear discriminant analysis for improved large vocabulary continuous speech recognition," IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, USA, pp.13-16, 1992.
  19. Tin Kam Ho, "Random decision forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, Quebec, Canada, Vol.1, pp.278-282, 1995.
  20. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, Vol. 86, No.11, pp.2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791
  21. A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," NIPS, 2012.
  22. C. Szegedy et al., "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp.1-9, 2015.
  23. P. Bas, T. Filler and T. Pevny, "Break our steganographic system -the ins and outs of organizing BOSS," Information Hiding 13th International Conference, Vol.6958, pp.59-70, 2011.
  24. S. Ioffe and C. Szegedy, "Batch normalization: acceleration deep network training by reducing internal covariate shift," 2015, https://arxiv.org/abs/1502.03167.
  25. K. He, X. Zhang, S. Ren and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," ECCV, pp.346-361, 2014.
  26. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, Vol.20, pp.273-297, 1995. https://doi.org/10.1007/BF00994018
  27. X. Song, F. Liu, C. Yang, X. Luo and Y. Zhang, "Steganalysis of adaptive JPEG steganography using 2D Gabor filters," in Proc. 3rd ACM Inf. Hiding Multimedia Secur. Workshop, pp.15-23, 2015.
  28. DDE LAB download, http://dde.binghamton.edu/download/
  29. Tensorflow, https://www.tensorflow.org/
  30. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," ICLR, 2015.
  31. The cifar-10 dataset, https://www.cs.toronto.edu/-kriz/cifar.html
  32. ILSVRC, http://image-net.org
  33. J. Sanchez and F. Perronnin, "High-dimensional signature compression for large-scale image classification," CVPR 2011, Colorado Springs, CO, USA, pp.1665-1672, 2011.
  34. S. Hochreiter, Y. Bengio, P. Frasconi and J. Schmidhuber, "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies," in A Field Guide to Dynamical Recurrent Networks, IEEE, 2001, doi: 10.1109/9780470544037.
  35. S. Kong and M. Takatsuka, "Hexpo: A vanishing-proof activation function," 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp.2562-2567, 2017.
  36. J. Ye, J. Ni and Y. Yi, "Deep learning hierachical representations for image steganalysis," IEEE Transactions on Information Forensics and Security, Vol.12, No.11, pp.2545-2557, 2017. https://doi.org/10.1109/TIFS.2017.2710946
  37. J. Kim and H. Park, "Image steganography using layered pixel-value differencing," Jounal of Broadcasting Engineering, Vol.22, No.3, 2017.
  38. J. Kim and H. Park, "A statistical approach for improving the embedding capacity of block matching based image steganography," Journal of Broadcast Engineering, Vol.22, No.5, 2017.
  39. J. Kim, H. Park, J. Park, "Experimental verification of the versatility of SPAM-based image steganalysis," Journal of Broadcast Engineering, Vol.23, No.4, pp.525-535, 2018.