Advertisement

Real-time estimation for the parameters of Gaussian filtering via deep learning

  • Feng Ding
  • Yuxi Shi
  • Guopu ZhuEmail author
  • Yun-qing Shi
Special Issue Paper
  • 47 Downloads

Abstract

Driven by the development of digital technology, manipulation towards digital images becomes simpler than ever before in recent years. Many smartphone applications bring the convenience for ordinary people to edit images in real-time without any professional skills. The digital forensics is an important research field in information security against the situation. In image forensics, it is necessary to validate all possible manipulation during the forming history of given images. Thus, many image forensics researchers focus on detecting certain manipulations to protect the integrity of images such as verifying Gaussian filtering. However, these works tend to make binary classification that if the image is processed by certain manipulation or not. The classification of same manipulation based on parameters are ignored. Here, we propose a method to estimate the parameters of Gaussian filtering to process images based on convolutional neural networks (CNN). Besides, in the modern world, it is also extremely important to enable the simulation in real-time to process with the given data immediately. The proposed method can also validate the given image in a quite short time. Our experiments show that the proposed method can provide excellent real-time performance in estimating the window size and standard deviation of Gaussian filterings. The well-trained model can satisfy us with not only the estimation accuracy, but also the validation time simultaneously.

Keywords

Image forensics Real-time Convolutional neural network Gaussian filtering 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61872350 and Grant 61572489, in part by the Youth Innovation Promotion Association of CAS under Grant 2015299, in part by the Basic Research Program of Shenzhen under Grant JCYJ20170818163403748, and in part by the Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence.

References

  1. 1.
    Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)CrossRefGoogle Scholar
  2. 2.
    Fridrich, J.: Digital image forensics. IEEE Signal Process. Mag. 26, 2 (2009)CrossRefGoogle Scholar
  3. 3.
    Piva, A.: An overview on image forensics. ISRN Signal Process 2013 (2013)Google Scholar
  4. 4.
    Swaminathan, A., et al.: Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forensic. Secur. 3.1, 101–117 (2008)CrossRefGoogle Scholar
  5. 5.
    Qi, L., et al.: Time-aware distributed service recommendation with privacy-preservation. Inf. Sci. 480, 354–364 (2019)CrossRefGoogle Scholar
  6. 6.
    Qi, L., et al.: A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Fut. Gen. Comput. Syst. 88, 636–643 (2018)CrossRefGoogle Scholar
  7. 7.
    Jung, K.-H., Yoo, K.-Y.: Steganographic method based on interpolation and LSB substitution of digital images. Multimed. Tools Appl. 74(6), 2143–2155 (2015)CrossRefGoogle Scholar
  8. 8.
    Meng, R., Rice, S.G., Wang, J., Sun, X.: A fusion steganographic algorithm based on faster R-CNN. Comput. Mater. Contin. 55(1), 1–16 (2018)Google Scholar
  9. 9.
    Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Trans. Inf. Forensic. Secur. 1(1), 111–119 (2006)CrossRefGoogle Scholar
  10. 10.
    Silva, E., Carvalho, T., Ferreira, A., Rocha, A.: Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J. Vis. Commun. Image Rep. 29, 16–32 (2015)CrossRefGoogle Scholar
  11. 11.
    Lee, J.-C.: Copy-move image forgery detection based on Gabor magnitude. J. Vis. Commun. Image Rep. 31, 320–334 (2015)CrossRefGoogle Scholar
  12. 12.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensic. Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  13. 13.
    Li, C.-T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensic. Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  14. 14.
    Ding, F., Zhu, G., Yang, J., Xie, J., Shi, Y.-Q.: Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Process. Lett. 22(3), 327–331 (2014)CrossRefGoogle Scholar
  15. 15.
    Ding, F., Zhu, G., Dong, W., Shi, Y.-Q.: An efficient weak sharpening detection method for image forensics. J. Vis. Commun. Image Rep. 50, 93–99 (2018)CrossRefGoogle Scholar
  16. 16.
    Zhu, N., Deng, C., Gao, X.: Image sharpening detection based on multiresolution overshoot artifact analysis. Multimed. Tools Appl. 76(15), 16563–16580 (2017)CrossRefGoogle Scholar
  17. 17.
    Guo, J.-M., Le, T.-N.: Secret communication using JPEG double compression. IEEE Signal Process. Lett. 17(10), 879–882 (2010)CrossRefGoogle Scholar
  18. 18.
    Barni, M., Bondi, L., Bonettini, N., Bestagini, P., Costanzo, A., Maggini, M., Tondi, B., Tubaro, S.: Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Rep. 49, 153–163 (2017)CrossRefGoogle Scholar
  19. 19.
    Yang, J., et al.: An effective method for detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forensic. Secur. 9.11, 1933–1942 (2014)CrossRefGoogle Scholar
  20. 20.
    Zhang, Y., Li, S., Wang, S., Shi, Y.Q.: Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process. Lett. 21(3), 275–279 (2014)CrossRefGoogle Scholar
  21. 21.
    Boroumand, M., Fridrich, J.: Scalable processing history detector for JPEG images. Electron. Imaging 2017(7), 128–137 (2017)CrossRefGoogle Scholar
  22. 22.
    Cao, G. et al.: Forensic detection of median filtering in digital images. Multimedia and Expo (ICME), 2010 IEEE International Conference on. IEEE (2010)Google Scholar
  23. 23.
    Ravi, H., Subramanyam, A.V., Emmanuel, S.: ACE—an effective anti-forensic contrast enhancement technique. IEEE Signal Process. Lett. 23(2), 212–216 (2015)CrossRefGoogle Scholar
  24. 24.
    Stamm, M.C. et al.: Undetectable image tampering through JPEG compression anti-forensics. Image Processing (ICIP), 2010 17th IEEE International Conference on IEEE (2010)Google Scholar
  25. 25.
    Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensic. Secur. 10(3), 507–518 (2014)Google Scholar
  26. 26.
    Xu, J., Ling, Y., Zheng, X.: Forensic detection of Gaussian low-pass filtering in digital images. In: IEEE, 2015 8th International Congress on Image and Signal Processing (CISP), pp. 819–823 (2015)Google Scholar
  27. 27.
    Rhee, K.H.: Gaussian filtering detection using band pass residual and contrast of forgery image. In: Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016 IEEE 7th Annual, IEEE (2016)Google Scholar
  28. 28.
    Paszke, A. et al.: Enet: a deep neural network architecture for real-time semantic segmentation. arXiv:1606.02147 (2016)
  29. 29.
    Xu, G., et al.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23.5, 708–712 (2016)CrossRefGoogle Scholar
  30. 30.
    Chen, J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22.11, 1849–1853 (2015)CrossRefGoogle Scholar
  31. 31.
    Bondi, L., et al.: First steps toward camera model identification with convolutional neural networks. IEEE Signal Process. Lett. 24.3, 259–263 (2017)CrossRefGoogle Scholar
  32. 32.
    Cui, Q., McIntosh, S., Sun, H.: Identifying materials of photographic images and photorealistic computer generated graphics based on deep CNNs. Comput. Mater. Contin. 55(2), 229–241 (2018)Google Scholar
  33. 33.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  34. 34.
    Ding, F. et al.: Smoothing identification for digital image forensics. Multimed. Tools Appl. pp. 1–21 (2018)Google Scholar
  35. 35.
    Yang, J., et al.: Estimating JPEG compression history of bitmaps based on factor histogram. Digit. Signal Process. 41, 90–97 (2015)CrossRefGoogle Scholar
  36. 36.
    Zhou, Z. et al.: Multiple distance-based coding: toward scalable feature matching for large-scale web image search. IEEE Trans. Big Data (2019)Google Scholar
  37. 37.
    Ye, J., Shen, Z., Behrani, P., Ding, F., Shi, Y.-Q.: Detecting USM image sharpening by using CNN. Signal Process. Image Commun. 68, 258–264 (2018)CrossRefGoogle Scholar
  38. 38.
    Yuan, C., Li, X., Wu, Q.M.J., Li, J., Sun, X.: Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis. Comput. Mater. Contin. 53(3), 357–371 (2017)Google Scholar
  39. 39.
    Slavkovikj, V. et al.: Hyperspectral image classification with convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, ACM (2015)Google Scholar
  40. 40.
    Bas, P., Filler, T., Pevný, T.: “ Break Our Steganographic System”: The Ins and Outs of Organizing BOSS. International Workshop on Information Hiding. Springer, Berlin (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Osaka UniversityOsakaJapan
  2. 2.New Jersey Institute of TechnologyNewarkUSA
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

Personalised recommendations