Skip to main content
SpringerLink
Log in

Development of secrete images in image transferring system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper addresses a model to secrete the information of one image under another without losing quality of image. Different approaches have been utilized for image hiding as needed, but multiple images maintain secrecy with information under another image is a challenging task. Thus, the framework is proposed to sustain the secrecy of an original image from another image. The proposed system collects random images through ImageNet and uses them as per the requirements of secrete images. The framework is used the deep neural networks method to build secrete information of multiple images under a single image. The enormous transfer of images is used to select standard image modifications using advanced deep learning approaches. It develops the significance of the critical framework that alleviates the choice of finding the hidden image information. Two vital methods such as Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) are used to find out difference between host and secret image by their corresponding evaluation scores. It produces the confidentiality of the image with the help of the host image. Therefore, data from several images are protected under a single image. The different image data are experimented with good performance. For comparative analysis, the accuracy is better in retrieving two secrete images on all experiments, like approximate accuracy is 100%. Still, when we considered PSNR and SSIM scores on the same two secrete images, accuracy became less than 50%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the first author upon reasonable request.

Code availability

The code is available from the first author upon reasonable request.

References

  1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M,Ghemawat S, Irving G, Isard M et al. (2016) Tensorflow: a system forlarge-scale machine learning. In OSDI, vol. 16, pp. 265–283

  2. Aslam MA, Azam MRF, Abbas M, Rasheed Y, Alotaibi SS, Anwar MW (2022) Image Steganography using Least Significant Bit (LSB) - A Systematic Literature Review. 2022 2nd international conference on computing and information technology (ICCIT), Tabuk, Saudi Arabia, pp. 32–38

  3. Baldi P, Hornik K (1989) Neural networks and principal component analysis. Neural Netw 2(1):53–58

    Article  Google Scholar 

  4. Baluja S (2020) Hiding images within images. IEEE Trans Pattern Anal Mach Intell 42(7):1685–1697

    Article  Google Scholar 

  5. Baluja S, Covell M (2008) Wave print: efficient wavelet-based audio finger printing. Pattern Recogn 41(11):3467–3480

    Article  MATH  Google Scholar 

  6. Bhuyan HK, Kamila NK (2014) Privacy preserving sub-feature selection based on fuzzy probabilities. Cluster Computing Springer 17(4):1383–1399

    Article  Google Scholar 

  7. Bhuyan HK, Kamila NK (2015) Privacy preserving sub-feature selection in distributed data mining. Applied Soft Computing Elsevier 36:552–569

    Article  Google Scholar 

  8. Bhuyan HK, Kamila NK, Dash SK (2011) An approach for privacy preservation of distributed data in peer-to-peer network using multiparty computation. Int Journal Comput Sci Issues (IJCSI) 3(8):424–429

    Google Scholar 

  9. Bhuyan HK, Dash SK, Roy S, Swain DK (2012) Privacy Preservation with Penalty in Decentralized Network using Multiparty Computation. Int J Advanc Comput Technol (IJACT) 4(1):297–303

    Article  Google Scholar 

  10. Bhuyan HK, Kamila NK, Jena LD (2016) Pareto-based multi-objective optimization for classification in data mining. Cluster Computing (Springer) 19(4):1723–1745

    Article  Google Scholar 

  11. Bhuyan HK, Kumar LR, Reddy RK (2019) Optimization model for Sub-feature selection in data mining, 2nd International Conference on Smart Systems and Inventive Technology (ICSSIT 2019), IEEE Explore, pp 1212–1216

  12. Bhuyan HK, Chakraborty C, Pani SK, Ravi VK (2021) Feature and sub-feature selection for classification using correlation coefficient and fuzzy model. IEEE Trans Eng Manag:1–15

  13. Bhuyan HK, Chakraborty C, Shelke Y, Pani SK (2021) COVID-19 diagnosis system by deep learning approaches. Expert Syst 39(3):1–18

    Google Scholar 

  14. Bhuyan HK, Kamila NK, Pani SK (2021) Individual privacy in data mining using fuzzy optimization, engineering optimization. Pp. 1-19 (early published)

  15. Burke J, King S (2022) Edge tracing using Gaussian process regression. IEEE Trans Image Process 31:138–148

    Article  Google Scholar 

  16. Chandra K, Kapoor G, Kohli R, Archana G. (2016) Improving software quality using machine learning. In: 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH). Pp. 115-118

  17. Chaumont M, Puech W, Lahanier C (2013) Securing color information of an image by concealing the color palette. J Syst Softw 86(3):809–825

    Article  Google Scholar 

  18. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deep lab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  19. Fridrich J, Goljan M, Du R (2001) Detecting LSB steganography in color, and gray-scale images. IEEE Multimed 8(4):22–28

    Article  Google Scholar 

  20. Fridrich J, Goljan M, Soukal D (2004) Searching for the stego-key. In Proceedings of SPIE, vol. 5306, pp. 70–82

  21. Gong Y, Lazebnik S, Gordo A, Perronnin F (2013) Iterative quantization: a procrustean approach to learning binary codes forlarge-scale image retrieval. IEEE Trans Pattern Anal Mach Intell 35(12):2916–2929

    Article  Google Scholar 

  22. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D,Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In Adv. in Neural Information Processing Systems, pp 2672–2680. https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html

  23. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MATH  Google Scholar 

  24. Hu D, Wang L, Jiang W, Zheng S, Li B (2018) A novel image steganography method via deep convolutional generative adversarial networks. IEEE Access 6:38303–38314

    Article  Google Scholar 

  25. Huang Z, Yang S, Zhou MC, Li Z, Zheng G, Chen Y (2022) Feature map distillation of thin nets for low-resolution object recognition. IEEE Trans Image Process 31:1364–1379

    Article  Google Scholar 

  26. Jain AK, Uludag U (2003) Hiding biometric data. IEEE Trans Pattern Anal Mach Intell 25(11):1494–1498

    Article  Google Scholar 

  27. Jarusek R, Volna E, Kotyrba M (2018) Robust steganographic method based on unconventional approach of neural networks. Appl Soft Comput 67:505–518

    Article  Google Scholar 

  28. Kessler GC (2014) An overview of steganography for the computer forensics examiner. Forensic Sci Commun 6(3):1–29

    Google Scholar 

  29. Kingma D, Adam JB (2015) A method for stochastic optimization. in ICLR, pp 1–15

  30. Korshunov P, Marcel S (2017) Impact of score fusion on voice biometrics and presentation attack detection in cross-database evaluations. IEEE J Select Top Signal Process 11(4):695–705

    Article  Google Scholar 

  31. Larsen ABL, Sønderby SK, Winther O (2015) Autoencoding beyond pixels using a learned similarity metric. Pp 1–8, arXiv:1512.09300

  32. Li S, Li C, Lo K-T, Chen G (2008) Cryptanalysis of an image scrambling scheme without bandwidth expansion. IEEE Trans Circuits Syst Vid Technol 18(3):338–349

    Article  Google Scholar 

  33. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform. Multim Tools Appl 76:20739–20753

    Article  Google Scholar 

  34. Liao X, Yu Y, Li B, Li Z, Zheng Q (2020) A new payload partition strategy in color image steganography. IEEE Transact Circuits Syst Vid Technol 30(3):685–696

    Article  Google Scholar 

  35. Liao X, Yin J, Chen M, Zheng Q (2020) Adaptive payload distribution in multiple images steganography based on image texture features, IEEE transactions on dependable and secure computing. Pp. 1-14

  36. Liu Q, Sung AH (2008) Image complexity and feature mining for steganalysis of least significant bit matching steganography. Inf Sci 178(1):21–36

    Article  Google Scholar 

  37. Ma H, Liu S, Liao Q, Zhang J, Xue J-H (2022) Defocus image Deblurring network with defocus map estimation as auxiliary task. IEEE Trans Image Process 31:216–226

    Article  Google Scholar 

  38. Miao J, Kou KI (2022) Color image recovery using low-rank quaternion matrix completion algorithm. IEEE Trans Image Process 31:190–201

    Article  Google Scholar 

  39. Pibre L, Pasquet J, Ienco D, Chaumont M (2016) Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source mismatch. Electron Imaging 2016(8):1–11

    Article  Google Scholar 

  40. Qamra A, Meng Y, Chang EY (2005) Enhanced perceptual distance functions and indexing for image replica recognition. IEEE Trans Pattern Anal Mach Intell 27(3):379–391

    Article  Google Scholar 

  41. Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099

    Article  Google Scholar 

  42. Trung V, Lai P, Raich R, Pham A, Fern XZ, Arvind Rao UK (2020) A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis. IEEE Trans Med Imaging 39(10):3125–3136

    Article  Google Scholar 

  43. Van De Ville D, Philips W, Van de Walle R, Lemahieu I (2004) Image scrambling without bandwidth expansion. IEEE Trans On Circuits and Sys Vid Technol 14(6):892–897

    Article  Google Scholar 

  44. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR 11(Dec):3371–3408

    MATH  Google Scholar 

  45. Vu MH, Löfstedt T, TufveNyholm RS (2020) A question-centric model for visual question answering in medical imaging. IEEE transactions on medical imaging, volume: 39. Issue 9:2856–2868

    Google Scholar 

  46. Wang Y, Moulin P (2008) Perfectly secure steganography: capacity, error exponents, and code constructions. IEEE Trans Inform Theory Special Issue Security 55(6):2706–2722

    Article  MATH  Google Scholar 

  47. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  48. Wang G, Hoiem D, Forsyth D (2012) Learning image similarity from flickr groups using fast kernel machines. IEEE Trans Pattern Anal Mach Intell 34(11):2177–2188

    Article  Google Scholar 

  49. Wang WZX, You W, Chen J, Dai P, Zhang P (2019) RESLS: region and edge synergetic level set framework for image segmentation. IEEE Trans Image Process 29:57–71

    MATH  Google Scholar 

  50. Xu W, Wang G (2022) A domain gap aware generative adversarial network for multi-domain image translation. IEEE Trans Image Process 31:72–84

    Article  Google Scholar 

  51. Yedroudj M, Comby F, Chaumont M, Yedrouj-net (2018) An efficient CNN for spatial steganalysis. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP’2018, pp 2092–2096

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, Andhra Pradesh, India

    Hemanta Kumar Bhuyan & A. Vijayaraj

  2. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, 34754, Saudi Arabia

    Vinayakumar Ravi

Authors
  1. Hemanta Kumar Bhuyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Vijayaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinayakumar Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinayakumar Ravi.

Ethics declarations

Conflicts of interest/competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhuyan, H.K., Vijayaraj, A. & Ravi, V. Development of secrete images in image transferring system. Multimed Tools Appl 82, 7529–7552 (2023). https://doi.org/10.1007/s11042-022-13677-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13677-3

Keywords

Search

Navigation