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Steganalysis aided by fragile detection of image manipulations

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Abstract

Steganalysis is usually considered as a two-class classification problem of differentiating between covers and stegos. However, in the real world, the cover image may have undergone various operations, which causes two problems that some processed covers tend to be judged as stegos by the steganalyzer and the stegos processed before information embedding may be easily missed, resulting in the high false alarm rate and the high missed detection rate of steganalysis respectively. To address the former problem, this paper proposed a steganalysis framework based on the combination of the image forensics and the steganalysis tools to reduce the false alarms. First, the fragile detection of image manipulations which is not robust to steganography is applied to separate the normally processed images from the investigated images. Then remaining images are fed to the trained classifier for stegnalysis. The experimental results on gamma transformed images validate the effectiveness of the proposed steganalysis framework that the false alarm rates of steganalysis can be reduced when the investigated image dataset contains normally processed images.

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  1. http://agents.fel.cvut.cz/stegodata/

References

  1. Barni M, Cancelli G, Esposito A (2010) Forensics aided steganalysis of heterogeneous images. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, ICASSP 2010. IEEE, Dallas, pp 1690–1693, DOI https://doi.org/10.1109/ICASSP.2010.5495494, (to appear in print)

  2. Bianchi T, Piva A (2012) Detection of nonaligned double jpeg compression based on integer periodicity maps. IEEE Trans Inf Forensics Secur 7(2):842–848. https://doi.org/10.1109/TIFS.2011.2170836

    Article  Google Scholar 

  3. Birajdar GK, Mankar VH (2014) Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of interpolation. AEU - Int J Electron Commun 68(7):644–652. https://doi.org/10.1016/j.aeue.2014.01.013

    Article  Google Scholar 

  4. Cao G, Zhao Y, Ni R (2010) Edge-based blur metric for tamper detection. J Inf Hiding Multimed Signal Process 1(1):20–27

    Google Scholar 

  5. Cao G, Zhao Y, Ni R (2010) Forensic estimation of gamma correction in digital images. In: Proceedings of the international conference on image processing, ICIP 2010. IEEE, Hong Kong, pp 2097–2100, DOI https://doi.org/10.1109/ICIP.2010.5652701, (to appear in print)

  6. Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9(3):515–525. https://doi.org/10.1109/TIFS.2014.2300937

    Article  Google Scholar 

  7. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27:1–27:27. https://doi.org/10.1145/1961189.1961199

    Google Scholar 

  8. Chen X, He F, Yu H (2018) A matting method based on full feature coverage. In: Multimedia tools and applications. https://doi.org/10.1007/s11042-018-6690-1

  9. Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for Steganalysis of digital images. In: 2014 IEEE international workshop on information forensics and security, WIFS 2014. https://doi.org/10.1109/WIFS.2014.7084302. IEEE, Atlanta, pp 48–53

  10. Denemark T, Fridrich J, Alfaro PC (2016) Improving selection-channel-aware steganalysis features. In: Alattar AM, Memon ND (eds) Proceedings of IS&T, electronic imaging, media watermarking, security, and forensics 2016. Ingenta, San Francisco, pp 1–8

  11. Fridrich J, Kodovský J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882. https://doi.org/10.1109/TIFS.2012.2190402

    Article  Google Scholar 

  12. Gonzalez RC, Woods RE (2007) Digital image processing. Prentice Hall, Upper Saddle River

    Google Scholar 

  13. He X, Liu F, Luo X, Yang C (2009) Classification between PS and Stego Images Based on Noise Model. In: 2009 third international conference on multimedia and ubiquitous engineering, MUE 2009. IEEE Computer Society, Qingdao, pp 31–36, DOI https://doi.org/10.1109/MUE.2009.16, (to appear in print)

  14. Holub V, Fridrich J (2013) Digital image steganography using universal distortion. In: ACM information hiding and multimedia security workshop, IH&MMSec ’13. https://doi.org/10.1145/2482513.2482514. http://doi.acm.org/10.1145/2482513.2482514. ACM, Montpellier, pp 59–68

  15. Hou X, Zhang T, Xiong G, Wan B (2012) Forensics aided steganalysis of heterogeneous bitmap images with different compression history. KSII Trans Internet Inf Syst 6(8):1926–1945. https://doi.org/10.3837/tiis.2012.08.003

    Google Scholar 

  16. Huang F, Huang J, Shi Y (2010) Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 5(4):848–856. https://doi.org/10.1109/TIFS.2010.2072921

    Article  Google Scholar 

  17. Ker AD, Bas P, Böhme R, Cogranne R, Craver S, Filler T, Fridrich J, Pevný T (2013) Moving steganography and steganalysis from the laboratory into the real world. In: ACM information hiding and multimedia security workshop, IH&MMSec ’13. ACM, Montpellier, pp 45–58, DOI https://doi.org/10.1145/2482513.2482965, (to appear in print)

  18. Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. In: Media forensics and security II, part of the IS&T-SPIE electronic imaging symposium. https://doi.org/10.1117/12.839100, vol 7541. SPIE, San Jose, p 754110

  19. Kodovský J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444

    Article  Google Scholar 

  20. Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. In: 2014 IEEE international conference on image processing, ICIP 2014. https://doi.org/10.1109/ICIP.2014.7025854. IEEE, Paris, France, pp 4206–4210

  21. Li W, Zhang T, Hu G, Xie K (2014) Image Pre-classification to improve accuracy of universal steganalysis. In: 2014 5th IEEE international conference on software engineering and service science, ICSESS 2014. IEEE, Beijing, pp 364–368

  22. Liu G, Wang J, Lian S, Dai Y (2013) Detect image splicing with artificial blurred boundary. Math Comput Model 57(11–12):2647–2659. https://doi.org/10.1016/j.mcm.2011.06.026

    Article  MATH  Google Scholar 

  23. Luo W, Huang F, Huang J (2010) Edge adaptive image steganography based on LSB matching revisited. IEEE Trans Inf Forensics Secur 5(2):201–214. https://doi.org/10.1109/TIFS.2010.2041812

    Article  Google Scholar 

  24. Lv X, He F, Cai W, Cheng Y (2018) Supporting selective undo of string-wise operations for collaborative editing systems. Futur Gener Comput Syst 82:41–62. https://doi.org/10.1016/j.future.2017.11.046. http://www.sciencedirect.com/science/article/pii/S0167739X1730897X

    Article  Google Scholar 

  25. Lv X, He F, Cheng Y, Wu Y (2018) A novel CRDT-based synchronization method for real-time collaborative CAD systems. Adv Eng Inf 38:381–391. https://doi.org/10.1016/j.aei.2018.08.008. http://www.sciencedirect.com/science/article/pii/S147403461730486X

    Article  Google Scholar 

  26. Niu Y, Zhao Y, Ni R (2017) Robust median filtering detection based on local difference descriptor. Signal Process Image Commun 53:65–72. https://doi.org/10.1016/j.image.2017.01.008

    Article  Google Scholar 

  27. Pevný T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224. https://doi.org/10.1109/TIFS.2010.2045842

    Article  Google Scholar 

  28. Pevný T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: Information hiding - 12th international conference, IH 2010. Springer, Calgary, pp 161–177

  29. Rosa AD, Fontani M, Massai M, Piva A, Barni M (2015) Second-order statistics analysis to cope with contrast enhancement counter-forensics. IEEE Signal Process Lett 22(8):1132–1136. https://doi.org/10.1109/LSP.2015.2389241

    Article  Google Scholar 

  30. Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5:492–506. https://doi.org/10.1109/TIFS.2010.2053202

    Article  Google Scholar 

  31. Sun X, Zhang W, Yu N, Wei Y (2017) Steganography based on parameters’ disturbance of spatial image transform. J Commun 38(10):166–174. ISSN= 1000–436X

    Google Scholar 

  32. Tang W, Li H, Luo W, Huang J (2014) Adaptive steganalysis against WOW embedding algorithm. In: ACM information hiding and multimedia security workshop, IH&MMSec ’14. https://doi.org/10.1145/2600918.2600935. ACM, Salzburg, pp 91–96

  33. Wan W, Wang J, Li J, Meng L, Sun J, Zhang H, Liu J (2018) Pattern complexity-based JND estimation for quantization watermarking. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2018.08.009. http://www.sciencedirect.com/science/article/pii/S0167865518304033

  34. Wan W, Wang J, Li J, Sun J, Zhang H, Liu J (2018) Hybrid JND model-guided watermarking method for screen content images. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6860-1

  35. Wang P, Liu F, Yang C, Luo X (2018) Blind forensics of image gamma transformation and its application in splicing detection. J Vis Commun Image Represent 55:80–90. https://doi.org/10.1016/j.jvcir.2018.05.020

    Article  Google Scholar 

  36. Wang P, Liu F, Yang C, Luo X (2018) Parameter estimation of image gamma transformation based on zero-value histogram bin locations. Signal Process Image Commun 64:33–45. https://doi.org/10.1016/j.image.2018.02.011

    Article  Google Scholar 

  37. Wu Y, He F, Zhang D, Li X (2018) Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput 11(2):341–353. https://doi.org/10.1109/TSC.2015.2501981. doi.ieeecomputersociety.org/10.1109/TSC.2015.2501981

    Article  Google Scholar 

  38. Yang J, Xie J, Zhu G, Kwong S, Shi Y (2014) An effective method for detecting double jpeg compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 9(11):1933–1942. https://doi.org/10.1109/TIFS.2014.2359368

    Article  Google Scholar 

  39. Yi Z, Fazhi HE, Qiu Y (2017) Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Sci China Inf Sci 60(6):68102

    Article  Google Scholar 

  40. Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77 (18):24097–24119. https://doi.org/10.1007/s11042-018-5697-y

    Article  Google Scholar 

  41. Yu H, He F, Pan Y (2018) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6735-5

  42. Yuan HD (2011) Blind forensics of median filtering in digital images. IEEE Trans Inf Forensics Secur 6(4):1335–1345

    Article  Google Scholar 

  43. Zhang S, He F, Ren W, Yao J (2018) Joint learning of image detail and transmission map for single image dehazing. Vis Comput https://doi.org/10.1007/s00371-018-1612-9

  44. Zhou L, Wang D, Guo Y, Zhang J (2007) Blur detection of digital forgery using mathematical morphology. In: Nguyen NT, Grzech A, Howlett RJ, Jain LC (eds) Agent and multi-agent systems: technologies and applications: first KES international symposium proceedings, KES-AMSTA 2007. Springer, Berlin, pp 990–998

  45. Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core SIMD CPUs. Futur Gener Comput Syst 79:473–487. https://doi.org/10.1016/j.future.2017.09.073. http://www.sciencedirect.com/science/article/pii/S0167739X16304289

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers for their thorough comments and suggestions that helped to improve this paper.

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Authors and Affiliations

  1. Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China

    Ping Wang, Fenlin Liu, Chunfang Yang & Xiangyang Luo

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  1. Ping Wang
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  2. Fenlin Liu
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  3. Chunfang Yang
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  4. Xiangyang Luo
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Correspondence to Fenlin Liu.

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This work was supported in part by the National Natural Science Foundation of China (No. 61772549, 61872448, U1736214, 61602508, and 61601517), and the National Key R&D Program of China(No. 2016YFB0801303 and 2016QY01W0105).

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Wang, P., Liu, F., Yang, C. et al. Steganalysis aided by fragile detection of image manipulations. Multimed Tools Appl 78, 23309–23328 (2019). https://doi.org/10.1007/s11042-019-7654-9

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