Abstract
Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.
Similar content being viewed by others
References
Abdulla AA, Sellahewa H, Jassim SA (2019) Improving embedding efficiency for digital steganography by exploiting similarities between secret and container images. Multimed Tools Appl 78:17799–17823. https://doi.org/10.1007/s11042-019-7166-7
Abraham J, Paul V (2019) An imperceptible spatial domain color image watermarking scheme. J King Saud Univ - Comput Inf Sci 31:125–133. https://doi.org/10.1016/j.jksuci.2016.12.004
Aini DN, Setiadi DRIM, Putro SN, et al (2019) Survey of methods in the spatial domain image steganography based imperceptibility and payload capacity. In: proceedings - 2019 international seminar on application for Technology of Information and Communication: industry 4.0: retrospect, Prospect, and challenges, iSemantic 2019. Institute of Electrical and Electronics Engineers Inc., Semarang, pp 434–439
Akbar JM, Setiadi DRIM (2019) Joint method using Akamatsu and discrete wavelet transform for image restoration. Appl Comput Inform https://doi.org/10.1016/J.ACI.2019.10.002, ahead-of-print
Al-Dmour H, Al-Ani A (2015) Quality optimized medical image steganography based on edge detection and hamming code. In: Proceedings - international symposium on biomedical imaging. IEEE Computer Society, New York, pp 1486–1489
Aqeel I, Raheel M (2019) Digital image steganography by using a hash based LSB (3-2-3) technique. In: Communications in Computer and Information Science. Springer Verlag, Bahawalpur, pp 713–724
Arun C, Murugan S (2018) Design of image steganography using LSB XOR substitution method. In: Proceedings of the 2017 IEEE international conference on communication and signal processing, ICCSP 2017. Institute of Electrical and Electronics Engineers Inc., Chennai, pp 674–677
Astuti YP, Setiadi DRIM, Rachmawanto EH, Sari CA (2018) Simple and secure image steganography using LSB and triple XOR operation on MSB. In: 2018 International conference on information and communications technology, ICOIACT 2018. Yogyakarta
Bovik AC (2009) The essential guide to image processing. Academic Press, Austin
Brunet D, Vrscay ER, Wang Z (2012) On the mathematical properties of the structural similarity index. IEEE Trans Image Process 21:1488–1495. https://doi.org/10.1109/TIP.2011.2173206
Chakraborty S, Jalal AS, Bhatnagar C (2017) LSB based non blind predictive edge adaptive image steganography. Multimed Tools Appl 76:7973–7987. https://doi.org/10.1007/s11042-016-3449-4
Chatterjee A, Ghosal SK, Sarkar R (2020) LSB based steganography with OCR: an intelligent amalgamation. Multimed tools Appl 1–19. https://doi.org/10.1007/s11042-019-08472-6
Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: survey and analysis of current methods. Signal Process 90:727–752
Darbani A, Alyannezhadi MM, Forghani M (2019) A new steganography method for embedding message in JPEG images. In: 2019 IEEE 5th conference on knowledge based engineering and innovation, KBEI 2019. Institute of Electrical and Electronics Engineers Inc., Tehran, pp 617–621
Douglas M, Bailey K, Leeney M, Curran K (2018) An overview of steganography techniques applied to the protection of biometric data. Multimed Tools Appl 77:17333–17373. https://doi.org/10.1007/s11042-017-5308-3
Grover R, Yadav DK, Chauhan DK, Kamya S (2018) Adaptive steganography via image complexity analysis using 3D color texture feature. In: 3rd international conference on innovative applications of computational intelligence on power, energy and controls with their impact on humanity, CIPECH 2018. Institute of Electrical and Electronics Engineers Inc., Ghaziabad, pp 125–129
Gupta A, Ahuja S (2018) An improved image steganography technique using block division least significant bit approach. In: Proceedings - IEEE 2018 international conference on advances in computing, communication control and networking, ICACCCN 2018. Institute of Electrical and Electronics Engineers Inc., Greater Noida, pp 335–339
Gutub A, Al-Ghamdi M (2020) Hiding shares by multimedia image steganography for optimized counting-based secret sharing. Multimed Tools Appl 1–35. https://doi.org/10.1007/s11042-019-08427-x
Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: 2010 20th international conference on pattern recognition. IEEE, Istanbul, pp 2366–2369
Horé A, Ziou D (2013) Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure? IET Image Process 7:12–24. https://doi.org/10.1049/iet-ipr.2012.0489
Hussain M, Wahab AWA, Bin IYI et al (2018) Image steganography in spatial domain: a survey. Signal Process Image Commun 65:46–66. https://doi.org/10.1016/j.image.2018.03.012
Islam AU, Khalid F, Shah M et al (2017) An improved image steganography technique based on MSB using bit differencing. In: 2016 6th international conference on innovative computing technology, INTECH 2016. Institute of Electrical and Electronics Engineers Inc., Dublin, pp 265–269
Kadhim IJ, Premaratne P, Vial PJ, Halloran B (2019) Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing 335:299–326. https://doi.org/10.1016/j.neucom.2018.06.075
Krasula L, Le Callet P, Fliegel K, Klima M (2017) Quality assessment of sharpened images: challenges, methodology, and objective metrics. IEEE Trans Image Process 26:1496–1508. https://doi.org/10.1109/TIP.2017.2651374
Lee CF, Weng CY, Chen KC (2017) An efficient reversible data hiding with reduplicated exploiting modification direction using image interpolation and edge detection. Multimed Tools Appl 76:9993–10016. https://doi.org/10.1007/s11042-016-3591-z
Li X, Wang W, Wang W, Ding XL, Yin Q (2014) Optimal estimates of common remainder for the robust Chinese remainder theorem. Commun Nonlinear Sci Numer Simul 19:2373–2381. https://doi.org/10.1016/J.CNSNS.2013.10.034
Liao X, Yin J, Chen M, Qin Z (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE trans dependable Secur Comput 1–1. https://doi.org/10.1109/tdsc.2020.3004708
Liao X, Yu Y, Li B, Li Z, Qin Z (2020) A new payload partition strategy in color image steganography. IEEE Trans Circuits Syst Video Technol 30:685–696. https://doi.org/10.1109/TCSVT.2019.2896270
Min X, Ma K, Gu K, Zhai G, Wang Z, Lin W (2017) Unified blind quality assessment of compressed natural, graphic, and screen content images. IEEE Trans Image Process 26:5462–5474. https://doi.org/10.1109/TIP.2017.2735192
Muhammad K, Ahmad J, Rehman NU, Jan Z, Sajjad M (2017) CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method. Multimed Tools Appl 76:8597–8626. https://doi.org/10.1007/s11042-016-3383-5
Mukherjee S, Sanyal G (2019) Edge based image steganography with variable threshold. Multimed Tools Appl 78:16363–16388. https://doi.org/10.1007/s11042-018-6975-4
Pak C, Kim J, An K, Kim C, Kim K, Pak C (2020) A novel color image LSB steganography using improved 1D chaotic map. Multimed Tools Appl 79:1409–1425. https://doi.org/10.1007/s11042-019-08103-0
Patel N, Meena S (2017) LSB based image steganography using dynamic key cryptography. In: 2016 International conference on emerging trends in communication technologies, ETCT 2016. Institute of Electrical and Electronics Engineers Inc., Dehradun
Rashid RD, Majeed TF (2019) Edge based image steganography: Problems and solution. In: 2019 3rd international conference on communications, signal processing, and their applications, ICCSPA 2019. Institute of Electrical and Electronics Engineers Inc., Sharjah
Rehman A, Wang Z (2012) Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans Image Process 21:3378–3389. https://doi.org/10.1109/TIP.2012.2197011
Setiadi DRIM (2019) Payload enhancement on least significant bit image steganography using edge area dilation. Intl J Electron Telecommun 65:295–300. https://doi.org/10.24425/ijet.2019.126313
Setiadi DRIM (2019) Improved payload capacity in LSB image steganography uses dilated hybrid edge detection. J King Saud Univ - Comput Inf Sci https://doi.org/10.1016/j.jksuci.2019.12.007
Setiadi DRIM, Santoso HA, Rachmawanto EH, Sari CA (2018) An improved message capacity and security using divide and modulus function in spatial domain steganography. In: 2018 international conference on information and communications technology (ICOIACT). IEEE, Yogyakarta, pp 186–190
Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15:430–444. https://doi.org/10.1109/TIP.2005.859378
Shukla AK, Pandey RK, Reddy PK (2020) Generalized fractional derivative based adaptive algorithm for image denoising. Multimed tools Appl 1–24. https://doi.org/10.1007/s11042-020-08641-y
Singh A, Singh H (2015) An improved LSB based image steganography technique for RGB images. In: Proceedings of 2015 IEEE international conference on electrical, computer and communication technologies, ICECCT 2015. Institute of Electrical and Electronics Engineers Inc., Coimbatore
Solomon C, Breckon T (2011) Fundamentals of digital image processing
Subhedar MS, Mankar VH (2019) Secure image steganography using framelet transform and bidiagonal SVD. Multimed tools Appl 1–22. https://doi.org/10.1007/s11042-019-08221-9
Subong RA, Fajardo AC, Kim YJ (2018) LSB Rotation and Inversion Scoring Approach to Image Steganography. In: Proceeding of 2018 15th international joint conference on computer science and software engineering, JCSSE 2018. Institute of Electrical and Electronics Engineers Inc., Nakhonpathom
Sudibyo U, Eranisa F, Rachmawanto EH, et al (2018) A secure image watermarking using Chinese remainder theorem based on haar wavelet transform. In: proceedings - 2017 4th international conference on information technology, computer, and electrical engineering, ICITACEE 2017
Sundararajan D (2017) Color image processing. In: Digital Image Processing. Springer Singapore, Singapore, pp 407–438
Tan HL, Li Z, Tan YH et al (2013) A perceptually relevant mse-based image quality metric. IEEE Trans Image Process 22:4447–4459. https://doi.org/10.1109/TIP.2013.2273671
Verma V, Muttoo SK, Singh VB (2019) Enhanced payload and trade-off for image steganography via a novel pixel digits alteration. Multimed tools Appl 1–20. https://doi.org/10.1007/s11042-019-08283-9
Wang W (2020) An efficient multiple-bit reversible data hiding scheme without shifting. Multimed Tools Appl 79:555–579. https://doi.org/10.1007/s11042-019-08065-3
Wang Z, Bovik AC (2009) Mean squared error: lot it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 26:98–117. https://doi.org/10.1109/MSP.2008.930649
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861
Wu D-C, Tsai W-H (2003) A steganographic method for images by pixel-value differencing. Pattern Recogn Lett 24:1613–1626. https://doi.org/10.1016/S0167-8655(02)00402-6
Acknowledgments
The author received no financial support for the research, authorship, and/or publication of this article.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Setiadi, D.I.M. PSNR vs SSIM: imperceptibility quality assessment for image steganography. Multimed Tools Appl 80, 8423–8444 (2021). https://doi.org/10.1007/s11042-020-10035-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10035-z