•  
  •  
 

Abstract

The expeditious growth of electronic commerce (e-commerce) fundamentally transformed business operations and consumer behavior by enabling businesses to reach a global market, streamline supply chains, and provide personalized shopping experiences. This shift necessitated reliable security measures to protect sensitive information during online transactions. However, existing approaches that combined encryption, steganography, and conventional compression techniques such as DCT and autoencoders were limited in preserving image quality and computational efficiency. To address this gap, the objective of this study was to integrate WebP compression into the cryptographic–steganographic pipeline in order to improve image quality, ensure payload integrity, and achieve efficiency suitable for real-time e-commerce systems. While cryptographic–steganographic pipelines combining asymmetric encryption, spatial embedding, and compression are well established, this study focused on demonstrating the comparative performance benefits of WebP compression over existing approaches such as the Discrete Cosine Transform (DCT) and autoencoders. To achieve this, an enhanced model was developed and evaluated by integrating the ElGamal cryptosystem for encryption, Least Significant Bit (LSB) steganography for embedding, and WebP for compression and optimization. In the developed workflow, transaction data was encrypted using ElGamal, embedded into cover images with LSB steganography, compressed using WebP, and at the receiver’s end the images were decoded, the ciphertext was extracted, and decrypted with the private key to recover the original data. This process ensured confidentiality, efficiency, and reliability in e-commerce communication. Empirical evaluation was conducted using key performance metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and entropy. Results showed low MSE values, PSNR values exceeding 79 dB, SSIM scores of 0.9999, and minimal entropy loss, confirming both high image quality retention and strong payload integrity. These results demonstrate that the developed model achieved superior image quality, high structural fidelity, zero payload errors, and efficient performance, confirming its suitability for securing sensitive e-commerce transactions. Compared with the base work of Kumbhakar et al. [36], which reported a PSNR of 54.9 dB, the developed WebP-based model demonstrated a significant improvement in image quality and efficiency. These findings established that while the cryptographic–steganographic pipeline itself was not novel, the integration of WebP compression provided superior performance compared to traditional DCT-based methods. The developed model therefore offered a practical and effective solution for safeguarding sensitive e-commerce transactions, addressing threats such as data interception, unauthorized access, and Man-in-the-Middle attacks.

References

1] Kumbhakar D, Sanyal K, Karforma S. An optimal and effi cient data security technique through crypto-stegano for E commerce. Multimed Tool Appl 2023;82(14):21005—18. https://doi.org/10.1007/s11042-023-14526-7.

[2] Ghosal I, Balaji K. The process of providing security pro tection in the Amazon E- commerce system. 4. 2022. p. 1—7 [Online]. Available: https://technoaretepublication.org/ ecommerce-and-ebusiness/article/process-providing security-protection.pdf.

[3] Dijesh P, Babu SS, Vijayalakshmi Y. Enhancement of e commerce security through asymmetric key algorithm. Comput Commun 2020;153(January):125—34. https://doi. org/10.1016/j.comcom.2020.01.033.

[4] Jan A, Parah SA, Hussan M,Malik BA.Doublelayer security using crypto-stego techniques: a comprehensive review. Health Technol 2022;12(1):9—31. https://doi.org/10.1007/ s12553-021-00602-1.

[5] Chun SH. E-commerce liability and security breaches in mobile payment for e-business sustainability. Sustain 2019; 11(3). https://doi.org/10.3390/su11030715.

[6] Akinola O, Asaolu O. A trust, privacy and security model for e-commerce in Nigeria. Niger J Technol 2023;42(1):152—9. https://doi.org/10.4314/njt.v42i1.19.

[7] Alizai ZA, Tahir H, Murtaza MH, Tahir S, McDonald Maier K. Key-based cookie-less session management framework for application layer security. IEEE Access 2019; 7:128544—54. https://doi.org/10.1109/ACCESS.2019.2940331.

[8] Gao F. Data encryption algorithm for e-commerce platform based on blockchain technology. Discret Contin Dyn Syst Ser S 2019;12(4—5):1457—70. https://doi.org/10.3934/dcdss. 2019100.

[9] Gu K, Yang L, Huang S, Chang Y, Data B, Zheng Y. E Commerce consumer privacy protection based on differ ential privacy E-Commerce consumer differential privacy privacy protection based on. 2019. https://doi.org/10.1088/ 1742-6596/1168/3/032084.

[10] Jiang Y, Wang C, Wang Y, Gao L. A privacy-preserving E Commerce system based on the blockchain technology. IWBOSE 2019- 2019 IEEE 2nd Int Work Blockchain Ori ented Softw Eng 2019:50—5. https://doi.org/10.1109/ IWBOSE.2019.8666470.

[11] Ibegbulem DJ. The protection of consumers’ personal data in the era of E- commerce in Nigeria BY. May; 2019.

[12] Hassan MA, Shukur Z, Hasan MK. An efficient secure electronic payment system for e-commerce. Computers 2020;9(3):1—13. https://doi.org/10.3390/computers9030066.

[13] Konoth RK, Fischer B, Fokkink W, Athanasopoulos E, Razavi K, Bos H. SecurePay: strengthening two-factor authentication for arbitrary transactions. Proc- 5th IEEE Eur Symp Secur Privacy, Euro S P 2020 2020:569—86. https://doi. org/10.1109/EuroSP48549.2020.00043.

[14] Pabian A, Pabian B, Reformat B. E-customer security as a social value in the sphere of sustainability. Sustain 2020; 12(24):1—14. https://doi.org/10.3390/su122410590.

[15] Xu B, HuangD,MiB.Smartcity-based e-commerce security technology with improvement of SET network protocol. Comput Commun 2020;154(December 2019):66—74. https:// doi.org/10.1016/j.comcom.2020.02.024.

[16] D’adamo I, Gonzalez-Sanchez R, Medina-Salgado MS, Settembre-Blundo D. E-commerce calls for cyber-security and sustainability: how European citizens look for a trusted online environment. Sustain 2021;13(12):1—17. https://doi. org/10.3390/su13126752.

[17] Farooq A, Seyedmahmoudian M, Horan B, Mekhilef S, Stojcevski A. Overview and exploitation of haptic tele weight device in virtual shopping stores. Sustain 2021; 13(13):1—13. https://doi.org/10.3390/su13137253.

[18] Lee H, Yeon C. Blockchain-based traceability for anti counterfeit in cross-border E-Commerce transactions. 2021. p. 1—20. AL-BAHIR (JOURNAL FOR ENGINEERING AND PURE SCIENCES) 2026;8:90—107 107

[19] Mohammed ZA, Tejay GP. Examining the privacy paradox through individuals' neural disposition in e-commerce: an exploratory neuroimaging study. Comput Secur 2021;104: 102201. https://doi.org/10.1016/j.cose.2021.102201.

[20] Nugier C, Leblanc-Albarel D, Blaise A, Masson S, Huynh P, Wandji Piugie YB. An upcycling tokenization method for credit card numbers. Proc 18th Int Conf Secur Cryptogr SECRYPT 2021 2021:15—25. https://doi.org/10.5220/ 0010508600150025.

[21] Wu Z, Shen S, Zhou H, Li H, Lu C, Zou D. An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl Base Syst 2021;220: 106952. https://doi.org/10.1016/j.knosys.2021.106952.

[22] Alamri M, Ykhlef M. Survey of credit card anomaly and fraud detection using sampling techniques. Electron 2022; 11(23). https://doi.org/10.3390/electronics11234003.

[23] Althunayyan M, Saxena N, Li S, Gope P. Evaluation of black-box web application security scanners in detecting injection vulnerabilities. Electron 2022;11(13):1—20. https:// doi.org/10.3390/electronics11132049.

[24] Gonçalves MJA, Pereira RH, Coelho MAGM. User reputa tion on E-Commerce: blockchain-based approaches. J Cybersecurity Priv 2022;2(4):907—23. https://doi.org/10. 3390/jcp2040046.

[25] Singh SP, Alotaibi Y, Kumar G, Rawat SS. Intelligent adaptive optimisation method for enhancement of infor mation security in IoT-Enabled environments. Sustain 2022; 14(20):1—23. https://doi.org/10.3390/su142013635.

[26] Gao X, Zhang W, Zhao B, Zhang J, Wang J, Gao Y. Product authentication technology integrating blockchain and traceability structure. Electron 2022;11(20):1—16. https://doi. org/10.3390/electronics11203314.

[27] Aljebreen M, Alrayes FS, Aljameel SS, Saeed MK. Political optimization algorithm with a hybrid deep learning assisted malicious URL detection model. Sustainability 2023;15(24): 16811. https://doi.org/10.3390/su152416811.

[28] Al-Zubaidie M, Shyaa GS. Applying detection leakage on hybrid cryptography to secure transaction information in E Commerce apps. Futur Internet 2023;15(8). https://doi.org/ 10.3390/fi15080262.

[29] Burkhardt G, Boy F, Doneddu D, Hajli N. Privacy behaviour: a model for online informed consent. J Bus Ethics 2023; 186(1):237—55. https://doi.org/10.1007/s10551-022-05202-1.

[30] Feng Z, Li W, Zhang H, Zhang X. A framework of a block chain-supported remanufacturing trading platform through gap analysis. Sustain 2023;15(16). https://doi.org/10.3390/ su151612120.

[31] Li J, Wang Z, Yang J, Ye C, Che F. A semi-quantum private comparison base on W-States. Entropy 2023;25(9). https:// doi.org/10.3390/e25091269.

[32] Olawale YA, Salman A, Ishola AA. Customer satisfaction with e-Commerce business: a case of konga.com. Acta Univ Bohemiae Merid 2023;25(3):1—15. https://doi.org/10.32725/ acta.2022.018.

[33] Ramesh RK, Dodmane R, Shetty S, Aithal G, Sahu M, Sahu AK. A novel and secure fake-modulus based Rabin-Ӡ cryptosystem. Cryptography 2023;7(3). https://doi.org/10. 3390/cryptography7030044.

[34] Shyaa GS, Al-Zubaidie M. Utilizing trusted lightweight ci phers to support electronic-commerce transaction cryptog raphy. Appl Sci 2023;13(12). https://doi.org/10.3390/app1312 7085.

[35] Saeed S. A customer-centric view of E-Commerce security and privacy. Appl Sci 2023;13(2). https://doi.org/10.3390/app 13021020.

[36] Shetty PK, Prasad SHC, Kamath RC, Agarwal A, Kishan AS, Mishra L. Heuristic exploration of vital parameters for cash transactions through mobiles in the Coastal Hinterland of India. Eng Proc 2023;59(1). https://doi.org/10.3390/engproc 2023059022.

[37] Taherdoost H, Madanchian M. Blockchain-based E-Com merce: a review on applications and challenges. Electron 2023;12(8):1—17. https://doi.org/10.3390/electronics1208 1889.

[38] Aburbeian AM, Fernandez-Veiga M. Secure internet f inancial transactions: a framework integrating multi-factor authentication and machine learning. Ai 2024;5(1):177—94. https://doi.org/10.3390/ai5010010.

[39] Albshaier L, Almarri S, Hafizur Rahman MM. A review of blockchain's role in E-Commerce transactions: open chal lenges, and future research directions. Computers 2024; 13(1). https://doi.org/10.3390/computers13010027.

[40] Jou YT, Saflor CS, Mari~nas KA, Manzano HM, Uminga JM, Verde NA, et al. An integrated multi-criteria decision analysis and structural equation modeling application for the attributes influencing the customer's satisfaction and trust in E-Commerce applications. Sustain 2024;16(5). https://doi.org/10.3390/su16051727.

[41] Shah SP, Deshpande AV. Enforcing cybersecurity con straints for LLM-driven robot agents for online transactions. arXiv preprint arXiv:2503.15546 2025. Available: https:// arxiv.org/abs/2503.15546.

[42] Saïd BA. FDCT-based watermarking for robust and imperceptible medical image protection. Intelligence Based Med 2025;12:100280. https://doi.org/10.1016/j.ibmed. 2025.100280.

[43] Thomas S, Krishna A. A novel image compression method using wavelet coefficients and Huffman coding. J Eng Res 2023. https://doi.org/10.1016/j.jer.2023.08.015.

[44] Das S, Biswas P, Kar N, Sahu AK. Implementation and analysis of digital watermarking techniques for multimedia authentication. In: Deb S, Sahu AK, editors. Securing the digital world: a comprehensive guide to multimedia secu rity. Routledge/Taylor & Francis; 2025. p. 18—35. https://doi. org/10.1201/9781032663647-2.

[45] Ogundokun RO, Abikoye OC, Ogundepo EA, Babatunde AN, Tosho AR, Sahu AK. Secured textual med ical information using a modified LSB image steganography technique. In: Deb S, Sahu AK, editors. Securing the digital world: a comprehensive guide to multimedia security. Routledge/Taylor & Francis; 2025. p. 85—98.

[46] Deb S, Sahu AK, editors. Securing the digital world: a comprehensive guide to multimedia security. Routledge/ Taylor & Francis; 2025. ISBN: 9781032663623.

[47] Thomas S, Krishna A, Govind S, Sahu AK. A novel image compression method using wavelet coefficients and huff man coding. Journal of Engineering Research Aug. 2023. https://doi.org/10.1016/j.jer.2023.08.015.

[48] Intelligence-Based Medicine. FDCT-based watermarking for robust and imperceptible medical image protection, 5; 2025. p. 100280. https://doi.org/10.1016/j.ibmed.2025.100280.

[49] Kaur N, Jindal S. Performance analysis of LSB image steg anography using various cover images. Multimed Tool Appl 2020;79:34393—409. https://doi.org/10.1007/s11042-020-09434-4.

[50] Provos N, Honeyman P. Detecting steganographic content on the internet. In: CITI technical report. University of Michigan; 2001.

[51] Deb S, Sahu AK, editors. Securing the digital world: a comprehensive guide to multimedia security. Routledge/ Taylor & Francis; 2025. ISBN: 9781032663623.

Download

Share

COinS