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A new steganography algorithm based on video sparse representation

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Abstract

Steganography has been a great interest since long time ago. There are a lot of methods that have been widely used since long past. Recently, there has been a growing interest in the use of sparse representation in signal processing. Sparse representation can efficiently model signals in different applications to facilitate processing. Much of the previous work was focused on image and audio sparse representation for steganography. In this paper, a new steganography scheme based on video sparse representation (VSR) is proposed. To exploit proper dictionary, KSVD algorithm is applied to DCT coefficients of Y component related to video (cover) frames. Both I and Q components of video frames are used for secure message insertion. The aim is to hide secret messages into non-zero coefficients of sparse representation of DCT called, I and Q video frames. Several experiments are performed to evaluate the performance of the proposed algorithm, in case of some metrics such as pick signal to noise ratio (PSNR), the hiding ratio (HR), bit error rate (BER) and similarity (Sim) of secret message, and also runtime. The simulation results show that the proposed method exhibits appropriate invisibility and robustness.

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References

  1. Abdulla AA, Sellahewa H, Jassim SA (2019) Improving embedding efficiency for digital steganography by exploiting similarities between secret and cover images. Multimed Tools Appl:1–25

  2. Ahani S, Ghaemmaghami S (2010) Image steganography based on sparse decomposition in wavelet space. IEEE International Conference on Information Theory and Information Security, Beijing, pp 632–637

    Google Scholar 

  3. Ahani S, Ghaemmaghami S (2015) Colour image steganography method based on sparse representation. IET Image Process 6:496–505

    Google Scholar 

  4. Ahani S, Ghaemmaghami S (2015) Color image steganography method based on sparse representation. IET Image Process (6):496–505

    Google Scholar 

  5. Ahani S, Ghaemmaghami S, Wang ZJ (2015) A Sparse Representation-Based Wavelet Domain Speech Steganography Method. IEEE/ACM Transactions on Audio, Speech, and Language Processing 1:80–91

    Google Scholar 

  6. Ahani S, Ghaemmaghami S, Wang ZJ (2015) A Sparse Representation-Based Wavelet Domain Speech Steganography Method. IEEE Trans Audio Speech Lang Process (1):80–91

  7. Akram MZ, Azizah AM, Shayma SM (2011) High watermarking capacity based on spatial domain technique. Inf Technol J 10(7):1367–1373

    Google Scholar 

  8. Aziz Sbai SM, Aissa-El-Bey A, Pastor D (2012) Underdetermined source separation of finite alphabet signals via l1 minimization. 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, pp 625–628

    Google Scholar 

  9. Blanchard JD, Tanner J (2015) Performance comparisons of greedy algorithms in compressed sensing. Numerical Linear Algebra with Applications (2):254–282

    MathSciNet  MATH  Google Scholar 

  10. Budhia U, Kundur D, Zourntos T (2006) Digital Video Steganalysis Exploiting Statistical Visibility in the Temporal Domain. IEEE Transactions on Information Forensics and Security (4):502–516

    Google Scholar 

  11. Cai J, Ji H, Liu C, Shen Z (2009) Blind motion deblurring from a single image using sparse approximation. IEEE Conference on Computer Vision and Pattern Recognition, Miami, pp 104–111

    Google Scholar 

  12. Cao X, Du L, Wei X, Meng D, Guo X (2015) High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation. IEEE Transactions on Cybernetics 5:1132–1143

    Google Scholar 

  13. Cao X, Du L, Wei X, Meng D, Guo X (2016) High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation. IEEE Transactions on Cybernetics 5:1132–1143

    Google Scholar 

  14. Chae JJ, Manjunath BS (1999) Data hiding in video. Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), Kobe, pp 311–315

    Google Scholar 

  15. Chandramouli R, Memon N (2001) Analysis of LSB image steganography techniques. IEEE International Conference on Image Processing:1019–1022

  16. Cheddad A, Condell J, Curran K, McKevitt P (2008) Biometric Inspired Digital Image Steganography”, 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems, pp. 159–168

  17. Cheddad A, Condell J, Curran K, McKevitt P (2010) Digital image steganography: Survey and analysis of current methods. Signal Process (3):727–752

    MATH  Google Scholar 

  18. Chen Y, Nasrabadi NM, Tran TD (2013) Hyperspectral Image Classification via Kernel Sparse Representation. IEEE Trans Geosci Remote Sens (1):217–231

    Google Scholar 

  19. Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell (4):834–848

    Google Scholar 

  20. Cong Y, Zhang S, Lian Y (2015) K-SVD Dictionary Learning and Image Reconstruction Based on Variance of Image Patches. 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, pp 254–257

    Google Scholar 

  21. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Trans Image Process (8):2080–2095

    MathSciNet  Google Scholar 

  22. Dai W, Milenkovic O (2009) Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inf Theory (5):2230–2249

    MathSciNet  MATH  Google Scholar 

  23. Dan W, Xia W, Guangyan W, Yan Z (2016) Speech enhancement based on Emd and compressed sensing. IEEE International Conference on Signal and Image Processing (ICSIP), Beijing, pp 699–702

    Google Scholar 

  24. Donoho DL, Tsaig Y, Drori I, Starck JL (2012) Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inf Theory:1094–1121

    MathSciNet  MATH  Google Scholar 

  25. Escoda OD, Vandergheynst P (2004) A Bayesian approach to video expansions on parametric over-complete 2-D dictionaries. IEEE 6th Workshop on Multimedia Signal Processing, Siena, pp 490–493

    Google Scholar 

  26. Etezadifar P, Farsi H (2017) Scalable video summarization via sparse dictionary learning and selection simultaneously. Multimed Tools Appl (6):7947–7971

    Google Scholar 

  27. Fadili JM, Starck J, Murtagh F (2009) Inpainting and Zooming Using Sparse Representations. Comput J , Oxford University Press (UK) 1:64–79

    Google Scholar 

  28. Fang DY, Chang LW (2006) Data hiding for digital video with phase of motion vector. IEEE International Symposium on Circuits and Systems:1422–1425

  29. Farsi H (2010) Improvement of minimum tracking in Minimum Statistics noise estimation method. Signal Processing: an International Journal (SPIJ) 4:17–22

    Google Scholar 

  30. Figueiredo MA, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing (4):586–597

    Google Scholar 

  31. Forster E, Lowe T, Wenger S, Magnor M (2015) RGB-guided depth map compression via Compressed Sensing and Sparse Coding. Picture Coding Symposium (PCS), Cairns, pp 1–4

    Google Scholar 

  32. Geng Q, Wright J (2014) On the local correctness of ℓ1-minimization for dictionary learning. IEEE International Symposium on Information Theory, Honolulu, pp 3180–3184

    Google Scholar 

  33. Gorodnitsky IF, Rao BD (1997) Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans Signal Process (3):600–616

    Google Scholar 

  34. Hasheminejad M, Farsi H (2016) Frame level sparse representation classification for speaker verification. Multimed Tools Appl 76:21211–21224

    Google Scholar 

  35. Hasheminejad M, Farsi H (2018) Sample-specific late classifier fusion for speaker verification. Multimed Tools Appl 77:15273–15289

    Google Scholar 

  36. Hosseini SM, Farsi H, SadoghiYazdi H (2009) Best Clustering Around the Color Images. International Journal of Computer and Electrical Engineering 1:20–24

    Google Scholar 

  37. Hu HT, Hsu LY (2015) Robust, transparent and high-capacity audio watermarking in DCT domain. Signal Process:226–235

    Google Scholar 

  38. Hu Y, Zhang C, Su Y (2007) Information Hiding Based on Intra Prediction Modes for H.264/AVC. IEEE International Conference on Multimedia and Expo, Beijing, pp 1231–1234

    Google Scholar 

  39. Hua G, Xiang Y, Bi G (2016) When Compressive Sensing Meets Data Hiding. IEEE Signal Processing Letters (4):473–477

    Google Scholar 

  40. Huang J, Xu Y, Zhu P, Wang Y (2014) An Improved Reconstruction Algorithm Based on Multi-candidate Orthogonal Matching Pursuit Algorithm. Seventh International Symposium on Computational Intelligence and Design, Hangzhou, pp 564–568

    Google Scholar 

  41. Jalali A, Farsi H, Ghaemmaghami S (2018) A steganalysis System Based on Double Sparse Representation Classification (DSRC). Multimed Tools Appl 11:16347–16366

    Google Scholar 

  42. Jamil T (1999) Steganography: the art of hiding information in plain sight. IEEE Potentials 1:10–12

    Google Scholar 

  43. Keshavarz SN, Hajizadeh S, Hamidi M, Omali MG (2010) A Novel UWB Pulse Waveform Design Method. Fourth International Conference on Next Generation Mobile Applications, Services and Technologies, Amman, pp 168–173

    Google Scholar 

  44. Keshavarz SN, Hamidi M, Khoshbin H (2010) A PSO-Based UWB Pulse Waveform Design Method. Second International Conference on Computer and Network Technology, Bangkok, pp 249–253

    Google Scholar 

  45. Keshavarz SN, Kakhki MA, Omali MG, Hamidi M (2010) A novel UWB pulse design method using particle swarm optimization algorithm. Sci Res Essays (5):3049–3058

  46. Krstulovic S, Gribonval R (2006) MPTK: Matching pursuit made tractable, vol 3. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, pp 496–499

    Google Scholar 

  47. Kumar V, Kumar D (2010) Performance evaluation of DWT based image steganography. IEEE 2nd International Advance Computing Conference (IACC), Patiala, pp 223–228

    Google Scholar 

  48. Kumar BS, Shree VU (2017) Encrypting Images by Patch-Level Sparse Representation for High Capacity Reversible Data Hiding. International Journal of Advanced Technology and Innovative Research (1):1–8

  49. Liu P, Tao Y, Zhao W, Tang X (2017) Abnormal crowd motion detection using double sparse representation. Journal of Neurocomputing, Elsevier:3–12

  50. Liu E, Temlyakov VN (2012) The orthogonal super greedy algorithm and applications in compressed sensing. IEEE Trans Inf Theory (4):2040–2047

    MathSciNet  MATH  Google Scholar 

  51. Liu Y, Wang Z (2015) Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Process (5):347–357

    Google Scholar 

  52. Lou DC, Liu JL, Tso HK (2008) Evolution of information – hiding technology. Information Security and Ethics: Concepts, Methodologies, Tools and Applications, New York, pp 438–450

    Google Scholar 

  53. Maechler P, Greisen P, Sporrer B, Steiner S, Felber N, Burg A (2010) Implementation of greedy algorithms for LTE sparse channel estimation. Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, pp 400–405

    Google Scholar 

  54. Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process:3397–3415

    MATH  Google Scholar 

  55. Mansouri J, Khademi M (2009) An adaptive scheme for compressed video steganography using temporal and spatial features of the video signal. Int J Imaging Syst Technol (4):306–315

  56. Masud Karim SM, Rahman MS, Hossain MI (2011) A new approach for LSB based image steganography using secret key. 14th International Conference on Computer and Information Technology (ICCIT 2011), Dhaka, pp 286–291

    Google Scholar 

  57. Mohamadzadeh S, Farsi H (2016) Content Based Video Retrieval Based on HDWT and Sparse Representation. Image Analysis and Stereology (2):67–80

    MathSciNet  MATH  Google Scholar 

  58. Mstafa RJ, Elleithy KM (2015) A New Video Steganography Algorithm Based on the Multiple Object Tracking and Hamming Codes. IEEE 14th International Conference on Machine Learning and Applications (ICMLA):335–340

  59. Mstafa RJ, Elleithy KM (2015) A novel video steganography algorithm in the wavelet domain based on the KLT tracking algorithm and BCH codes. IEEE Long Island Systems, Applications and Technology Conference (LISAT):1–7

  60. Mstafa RJ, Elleithy KM (2015) A high payload video steganography algorithm in DWT domain based on BCH codes (15, 11). International IEEE Wireless Telecommunications Symposium, New York:1–8

  61. Mstafa RJ, Elleithy KM (2016) A novel video steganography algorithm in DCT domain based on hamming and BCH codes. IEEE 37th Sarnoff Symposium:208–213

  62. Mstafa RJ, Elleithy KM (2016) A DCT-based robust video steganographic method using BCH error correcting codes. IEEE Long Island Systems, Applications and Technology Conference (LISAT):1–6

  63. Needell D, Tropp JA (2009) CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmon Anal (3):301–321

    MathSciNet  MATH  Google Scholar 

  64. Needell D, Vershynin R (2009) Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found Comput Math, Springer 9:317–334

    MathSciNet  MATH  Google Scholar 

  65. Pan JS, Li W, Yang CS, Yan L (2015) Image steganography based on subsampling and compressive sensing. Multimed Tools Appl:9191–9205

    Google Scholar 

  66. Patsakis C, Aroukatos N (2014) LSB and DCT steganographic detection using compressive sensing. Journal of Information Hiding and Multimedia Signal Processing 1:20–32

    Google Scholar 

  67. Raja KB, Chowdary CR, Venugopal KR, Patnaik LM (2005) A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images. 3rd International Conference on Intelligent Sensing and Information Processing, Bangalore, pp 170–176

    Google Scholar 

  68. Rajesh GR, Nargunam AS (2013) Steganography algorithm based on discrete cosine transform for data embedding into raw video streams. IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON), Chennai, pp 554–558

    Google Scholar 

  69. Rencker L, Wang W, Plumbley MD (2017) A greedy algorithm with learned statistics for sparse signal reconstruction. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, pp 4775–4779

    Google Scholar 

  70. RoselinKiruba R, Sree Sharmila T (2018) Hiding Data in Videos Using Optimal Selection of Key-Frames. International Conference on Computer, Communication, and Signal Processing (ICCCSP):1–6

  71. Saha B, Sharma S (2012) Steganographic Techniques of Data Hiding Using Digital Images. Def Sci J 1:11–18

    Google Scholar 

  72. Schepker HF, Dekorsy A (2011) Sparse multi-user detection for CDMA transmission using greedy algorithms. 8th International Symposium on Wireless Communication Systems (ISWCS), Aachen, pp 291–295

    Google Scholar 

  73. Singh A, Hariharan S (2017) Performance analysis of energy efficient algorithm for MIMO based CRN with antenna selection and maximal ratio combining. International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, pp 663–668

    Google Scholar 

  74. Song L, Peng J (2012) Dictionary Learning Research Based on Sparse Representation. International Conference on Computer Science and Service System, Nanjing, pp 14–17

    Google Scholar 

  75. Su P (2013) C., S., Lu, M. T., Wu, C. Y.: “A practical design of high-volume steganography in digital video files”. Multimed Tools Appl 2:247–266

    Google Scholar 

  76. Sun G, Meng L, Liu L, Tan Y, Zhang J, Zhang H (2018) KSVD-based Multiple Description Image Coding. IEEE Access Journal:1962–1972

  77. Sung T, Shieh Y, Yu C, Hsin H (2006) High-Efficiency and Low-Power Architectures for 2-D DCT and IDCT Based on CORDIC Rotation. Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06), Taipei, pp 191–196

    Google Scholar 

  78. Swanson MD, Zhu B, Tewfik AH (1997) Data hiding for video-in-video, vol 2. International Conference on Image Processing, Santa Barbara, pp 676–679

    Google Scholar 

  79. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory (12):586–597

    MathSciNet  MATH  Google Scholar 

  80. Tseng YC, Chen YY, Pan HK (2002) A secure data hiding scheme for binary images. IEEE Trans Commun 8:1227–1231

    Google Scholar 

  81. Wang H, Xia Y, Wang Z (2017) Dictionary learning-based image compression. IEEE International Conference on Image Processing (ICIP), Beijing, pp 3235–3239

    Google Scholar 

  82. Xi R (2010) Super resolution processing of SAR images by Matching Pursuit method based on Genetic Algorithm. 3rd International Congress on Image and Signal Processing, Yantai, pp 2066–2070

    Google Scholar 

  83. Xu Z, Sun J (2010) Image Inpainting by Patch Propagation Using Patch Sparsity. IEEE Trans Image Process (5):1153–1165

  84. Yadav P, Mishra N, Sharma S (2013) A secure video steganography with encryption based on LSB technique. IEEE International Conference on Computational Intelligence and Computing Research, Enathi, pp 1–5

    Google Scholar 

  85. Yan X, Yang B, Zhang W, Liu C, Wang Y (2016) An Improved Denoising Algorithm of Feather and Down Image Based on KSVD. 8th International Conference on Information Technology in Medicine and Education (ITME), Fuzhou, pp 419–423

    Google Scholar 

  86. Yao M, Qi M, Yi Y, Shi Y, Kong J (2015) An Improved Information Hiding Method Based on Sparse Representation. Math Probl Eng:1–10

    Google Scholar 

  87. Zaheer M, Qureshi I, Muzaffar Z, Aslam L (2017) Compressed Sensing Based Image Steganography System for Secure Transmission of Audio Message with Enhanced Security. International Journal of Computer Science and Network Security 7:133–141

    Google Scholar 

  88. Zhang Q, Liu Y, Blum RS, Han J, Tao D (2018) Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review”, Elsevier. Information Fusion Journal:57–75

    Google Scholar 

  89. Zhang S, Wang H, Huang W (2017) Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Journal of Cluster Computing:1–9

  90. Zhao Y, Li J, Zhong Z (2014) Group-based sparse coding dictionary learning for object recognition. IEEE Workshop on Advanced Research and Technology in Industry Applications, Ottawa, pp 331–334

    Google Scholar 

  91. Zheng A, Zhao Y, Li C, Tang J, Luo B (2018) Moving Object Detection via Robust Low-Rank and Sparse Separating with High-Order Structural Constraint. IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi’an, pp 1–6

    Google Scholar 

  92. Zhu X, Liu L, Wang X, Wang J (2016) Super-resolution reconstruction via multiple sparse dictionary combined with sparse coding. IEEE International Conference on Information and Automation (ICIA), Ningbo, pp 1720–1725

    Google Scholar 

  93. Zhu X, Tao J, Li B, Chen X, Li Q (2015) A novel image super-resolution reconstruction method based on sparse representation using classified dictionaries. IEEE International Conference on Information and Automation, Lijiang, pp 776–780

    Google Scholar 

  94. Zhu Y, Zhang X, Wen G, He W, Cheng D (2016) Double sparse-representation feature selection algorithm for classification. Multimed Tools Appl:17525–17539

    Google Scholar 

  95. Zibulevsky M, Elad M (2010) L1-L2 Optimization in Signal and Image Processing. IEEE Signal Process Mag (3):76–88

    Google Scholar 

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  1. Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

    Arash Jalali & Hassan Farsi

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  1. Arash Jalali
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  2. Hassan Farsi
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Jalali, A., Farsi, H. A new steganography algorithm based on video sparse representation. Multimed Tools Appl 79, 1821–1846 (2020). https://doi.org/10.1007/s11042-019-08233-5

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