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Adaptive video data hiding with low bit-rate growth based on texture selection and ternary syndrome-trellis coding

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Abstract

Data hiding aims to embed a secret payload into a cover object without introducing significant degradation of the cover. The resulting object containing hidden information, typically also called stego, will not arouse obvious suspicion from the monitor. A number of data hiding systems have been designed for digital images and only a few focus on video sequences. Actually, video is quite desirable for data hiding due to its large capacity for carrying a payload. It motivates us to present an adaptive data hiding scheme to video sequences in this paper. In the proposed scheme, the non-zero quantized Discrete Cosine Transform (QDCT) coefficients of intra-frames are used to embed the secret payload based on ternary syndrome-trellis coding (STC) equipped with a well-designed distortion function. In order to realize data hiding with low bit-rate growth, the non-zero QDCT coefficients in complex texture regions with high amplitude are further exploited for data embedding. Experimental results have shown that, comparing with a part of related works, the proposed work provides high embedding capacity and lower bit-rate growth. And, the video quality after data hiding can be kept with a high level. In addition, the proposed work does not expose suspicious histogram distribution and block characteristics, which has shown the superiority and applicability of the proposed work.

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References

  1. Aly HA (2011) Data hiding in motion vectors of compressed video based on their associated prediction error. IEEE Trans Inform Forensics Secur 6(1):14–18

    Article  Google Scholar 

  2. Chen Y, Wang H, Wu H, Liu Y (2019) An adaptive data hiding algorithm with low bit-rate growth for h.264/avc video stream. Multimed Tools Appl 77(15):20157–20175

    Article  Google Scholar 

  3. Chen Y, Wang H, Wu H, Wu Z, Li T, Malik A (2019) Adaptive video data hiding through cost assignment and STCs. IEEE Trans Depend Secur Comput (early access):1

  4. Chen Y, Wang H, Zhang X (2020) Reversible data hiding based on run-level coding in h.264/avc video streams. Comput Mater Continua 63(2):911–922

    Google Scholar 

  5. Fallahpour M, Shirmohammadi S, Ghanbari M (2015) A high capacity data hiding algorithm for h.264/avc video. Secur Commun Netw 8(16):2947–2955

    Article  Google Scholar 

  6. Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensics Secur 6(3–2):920–935

    Article  Google Scholar 

  7. Guo L, Ni J, Shi Y (2012) An efficient jpeg steganographic scheme using uniform embedding. In: Proceedings of IEEE International Workshop on Information Forensics and Security, Costa Adeje, Tenerife, Spain, pp 169-174

  8. Guo L, Ni J, Su W, Tang C, Shi Y (2015) Using statistical image model for jpeg steganography: uniform embedding revisited. IEEE Trans Inf Forensics Secur 10(12):2669–2680

    Article  Google Scholar 

  9. Harmsen J, Pearlman W (2003) Steganalysis of additive-noise modelable information hiding. In: Proceedings of the SPIE, security watermarking multimedia contents, Santa Clara, 131-142

  10. Holub V, Fridrich J (2012) Designing steganography distortion using directional filters. In: IEEE International Workshop on International Workshop on Information Forensics and Security, Costa Adeje, Tenerife, Spain, pp 234-239

  11. Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 1(1):1–13

    Google Scholar 

  12. Jindal H, Saxena S, Singh H (2014) Challenges and issues in underwater acoustic sensor networks: a review. In: Proceeding of International Conference Parallel, Distributed and Grid Computing, Solan pp 251-255

  13. Jindal H, Kasana S, Saxena S (2016) A novel image zooming technique using wavelet coefficients. In: Proceedings of International Conference on Recent Cognizance in Wireless Communication and Image Processing, New Delhi, pp 1-7

  14. Jindal H, Saxena S, Kasana S (2017) Sewage water quality monitoring framework using multi-parametric sensors. Wirel Pers Commun 97(1):881–913

    Article  Google Scholar 

  15. Jindal H, Saxena S, Kasana S (2017) Triangular pyramidal topology to measure temporal and spatial variations in shallow river water using ad-hoc sensors network. Ad Hoc Sensor Wireless Netw 39(1–4):1–35

    Google Scholar 

  16. Jindal H, Saxena S, Kasana S (2018) A sustainable multiparametric sensors network topology for river water quality monitoring. Wirel Netw 24(1):3241–3265

    Article  Google Scholar 

  17. Jindal H, Kasana S, Saxena S (2018) Underwater pipelines panoramic image transmission and refinement using acoustic sensors. Int J Wavelets Multiresolution Inf Process 16(3):1–37

    Article  MathSciNet  Google Scholar 

  18. Jindal H, Singh H, Bharti M (2018) Modified cuckoo search for resource allocation on social internet of things. In: Proceeding of International Conference Parallel. Distributed and Grid Computing, Solan, pp 465–470

    Google Scholar 

  19. Kaur S, Jindal H (2017) Enhanced image watermarking technique using wavelets and interpolation. Int J Image Graphics Signal Process 9(7):23–35

    Article  Google Scholar 

  20. Khosrvi MR, Samadi S (2019) Efficient payload communications for IoT-enabled ViSAR vehicles using discrete cosine transform-based quasi-sparse bit injection. EURASIP J Wirel Commun Netw 262(2019)

  21. Khosrvi MR, Samadi S (2019) Modified data aggregation for aerial ViSAR sensor networks in transform domain. In: Proceedings of International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, USA, 87-90

  22. Khosrvi MR, Samadi S (2020) Reliable data aggregation in internet of ViSAR vehicles using chained dual-phase adaptive interpolation and data embedding. IEEE Internet Things J 7(4):2603–2610

    Article  Google Scholar 

  23. Khosrvi MR, Yazdi M (2018) A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram computation and quartered interpolation with greedy weights. Neural Comput & Applic 30(7):2017–2028

    Article  Google Scholar 

  24. Kim H, Kang S (2018) Genuine reversible data hiding technology using compensation for H.264 bitstreams. Multimed Tools Appl 77(7):8043–8060

    Article  Google Scholar 

  25. Kim D, Choi Y, Kim H, Yoo J, Choi H, Seo Y (2010) The problems in digital watermarking into intra-frames of h.264/avc. Image Vis Comput 28(8):1220–1228

    Article  Google Scholar 

  26. Kim C, Shin D, Leng L, Yang C (2016) Lossless data hiding for absolute moment block truncation coding using histogram modification. J Real-Time Image Process 14:101–114

    Article  Google Scholar 

  27. Kim C, Shin D, Leng L, Yang C (2018) Separable reversible data hiding in encrypted halftone image. Displays 55:71–79

    Article  Google Scholar 

  28. Kim C, Yang C, Leng L (2020) High-capacity of data hiding based on an image compressed by ABTC-EQ. Electronics 9(4):644

    Article  Google Scholar 

  29. Leng L, Zhang J, Xu J (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: proceedings of international conference on information and communication technology convergence (ICTC), Jeju, South Korea, pp 467-471

  30. Leng L, Li M, Kim C, Bi X (2015) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76(1):333–354

    Article  Google Scholar 

  31. Li S, Zhang X (2019) Toward construction-based data hiding: from secrets to fingerprint images. IEEE Trans Image Process 28(3):1482–1497

    Article  MathSciNet  Google Scholar 

  32. Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. In: Proceedings of IEEE International Conference on Image Processing, Paris, pp 4206-4210

  33. Liu Y, Chen L, Hu M, Jia Z, Jia S, Zhao H (2016) A reversible data hiding method for H.264 with Shamir’s (t, n)-threshold secret sharing. Neurocomputing 188:63–70

    Article  Google Scholar 

  34. Ma X, Li Z, Tu H, Zhang B (2010) A data hiding algorithm for h.264/avc video streams without intra-frame distortion drift. IEEE Trans Circuits Syst Video Technol 20(10):1320–1330

    Article  Google Scholar 

  35. Mander K, Jindal H (2017) An improved image compression and decompression technique using block truncation and wavelets. Int J Image Graphics Signal Process 9(8):17–29

    Article  Google Scholar 

  36. Mehta S, Mangat K, Jindal H (2015) Procreation of hierarchical routing using AODV. Int J Eng Res Comput Sci Eng 2(8):47–50

    Google Scholar 

  37. Mittal A, Jindal H (2017) Novelty in image reconstruction using DWT and CLAHE. Int J Image Graphics Signal Process 5(9):28–34

    Article  Google Scholar 

  38. Mourya G, Jindal H, Saxena S (2015) Software perspective to underwater acoustic sensors network. In: Proceedings of International Conference on Next Generation Computing Technologies. Dehradun, p 187-191

  39. Pevny T, Fridrich J (2007) Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proceedings of Security, Steganography, and Watermarking of Multimedia Contents, San Jose, pp 650503

  40. Pevny T, Filler T, Bas P (2010) Using high dimensional image models to perform highly undetectable steganography. In: International Conference on Information Hiding, Calgary, AB, Canada, 161-177

  41. Sadek MM, Khalifa AS, Mostafa MGM (2015) Video steganography: a comprehensive review. Multimed Tools Appl 74(17):7063–7094

    Article  Google Scholar 

  42. Saxena S, Mehta D, Kaur J et al (2014) Acoustic communication characteristics in UWDBCSN. In: Proceeding of International Conference Parallel, vol 176-180. Distributed and Grid Computing, Solan

    Google Scholar 

  43. Tao J, Li S, Zhang X, Wang Z (2018) Towards robust image steganography. IEEE Trans Circuits Syst Video Technol 29(2):594–600

    Article  Google Scholar 

  44. Tew Y, Wong KS (2014) An overview of information hiding in h.264/avc compressed video. IEEE Trans Circuits Syst Video Technol 24(2):305–319

    Article  Google Scholar 

  45. Wang Z, Qian Z, Zhang X, Yang M, Ye D (2018) On improving distortion functions for JPEG steganography. IEEE Access 6:74917–74930

    Article  Google Scholar 

  46. Wiegand T, Sullivan GJ, Bjontegaard G, Luthra A (2003) Overview of the h.264/avc video coding standard. IEEE Trans Circuits Syst Video Technol 13(7):560–576

    Article  Google Scholar 

  47. Xiang Y, Huang J, Perez-Gonzalez F, Hua G, Malik H (2016) Latest advances and emerging applications of data hiding. IEEE Access 4:9740–9742

    Article  Google Scholar 

  48. Xu D, Wang R (2016) Two-dimensional reversible data hiding-based approach for intra-frame error concealment in h.264/avc. Signal Process Image Commun 47:369–379

    Article  Google Scholar 

  49. Xu D, Wang R, Wang J (2012) Prediction mode modulated data-hiding algorithm for h.264/avc. J Real-Time Image Proc 7(4):205–214

    Article  Google Scholar 

  50. Xu D, Wang R, Shi Y (2014) Data hiding in encrypted h.264/avc video streams by codeword substitution. IEEE Trans Inf Forensics Secur 9(4):596–606

    Article  Google Scholar 

  51. Xue Y, Zhou J, Zeng H, Zhong P, Wen J (2019) An adaptive steganographic scheme for h.264/avc video with distortion optimization. Signal Process Image Commun 76:22–30

    Article  Google Scholar 

  52. Yao Y, Zhang W, Yu N, Zhao X (2015) Defining embedding distortion for motion vector-based video steganography. Multimed Tools Appl 74(24):11163–11186

    Article  Google Scholar 

  53. You W, Yun C, Zhao X (2017) Information hiding using cavlc: misconceptions and a detection strategy. In: Proceedings of International Workshop on Digital Watermarking, pp 187-201

  54. Zhang X, Wang S (2006) Efficient steganographic embedding by exploiting modification direction. IEEE Commun Lett 10(11):781–783

    Article  Google Scholar 

  55. Zhang L, Zhao X (2016) An adaptive video steganography based on intra-prediction mode and cost assignment. In: Proceedings of International Workshop on Digital Watermarking, Beijing, China, 518-532

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Acknowledgments

This work was partly supported by the National Natural Science Foundation of China under grant numbers 61902235, U1636206, U1936214 and 61525203. It was also supported by “Chen Guang” project under grant number 19CG46, co-funded by the Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

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  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Qingyang Liu, Hanzhou Wu & Xinpeng Zhang

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  1. Qingyang Liu
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  2. Hanzhou Wu
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  3. Xinpeng Zhang
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Correspondence to Hanzhou Wu.

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Liu, Q., Wu, H. & Zhang, X. Adaptive video data hiding with low bit-rate growth based on texture selection and ternary syndrome-trellis coding. Multimed Tools Appl 79, 32935–32955 (2020). https://doi.org/10.1007/s11042-020-09613-y

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