A cryptography-oriented reversible data hiding scheme using median prediction and grayscale invariance for secure image steganography
Manmohan Sharmamanmohan.sharma@jaipur.manipal.eduDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Priti Sharmapritisharma@ndimdelhi.inDepartment of Computer ScienceNew Delhi Institute of ManagementGuru Gobind Singh Indraprastha UniversityTughlakabad Institutional Area, New Delhi, Delhi, 110062, IndiaView full profile → , Preeti Rathipreeti.rathi@krmangalam.edu.inDepartment of Computer ScienceSchool of Engineering & TechnologyK. R. Mangalam UniversityGurgaon, Haryana, 122103, IndiaView full profile → , Sandeep Kumarsandeepkumar@cet-gov.ac.inDepartment of Computer Science & EngineeringUniversity College of Engineering and TechnologyBikaner Technical UniversityBikaner, Rajasthan, 334004, IndiaView full profile → , Vibha Sonivibhasoni.29@gmail.comDepartment of ManagementJaipur School of BusinessJECRC UniversityJaipur, Rajasthan, 302022, IndiaView full profile → , *Prashant VatsCorresponding authorprashant.vats@jaipur.manipal.eduDepartment of Computer Science and EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jan 2026
- Published Online:
- 14 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2746
- Pages:
- 3197–3205
Abstract
Reversible data hiding in encrypted images (RDH-EI) has emerged as a critical paradigm for high-security image systems, where both data confidentiality and lossless image recovery are mandatory. This paper proposes a cryptography-oriented reversible data hiding scheme that integrates encryption-assisted embedding with median-based prediction and grayscale invariance to achieve high payload capacity, low distortion, and strong cryptanalytic robustness. In the proposed framework, the original grayscale image is first encrypted using a lightweight stream cipher to ensure visual confidentiality. A median predictor is then employed on spatially correlated pixels to generate compact prediction errors, which are adaptively modulated for reversible embedding. To preserve visual consistency and reduce embedding artifacts, a grayscale invariance constraint is imposed, ensuring that the pixel intensity transitions remain statistically stable after data insertion. The embedded bitstream is protected using a secret-key-controlled permutation, providing resistance against statistical and differential steganalysis. At the receiver side, the hidden data and the original image are perfectly recovered without auxiliary side information, demonstrating strict reversibility.
Keywords
Subject Classifications
References
[1] R. Wang, G. Wu, Q. Wang, L. Yuan, Z. Zhang, and G. Miao, “Reversible data hiding in encrypted images using median edge detector and two’s complement,” Symmetry, vol. 13, no. 6, Art. no. 921 (May 2021), doi: 10.3390/sym13060921.
[2] Z. Yin, B. Luo, W. Hong, Y. Ye, and Y. Peng, “Reversible data hiding in encrypted images based on pixel prediction and multi-MSB planes rearrangement,” arXiv preprint arXiv:2009.02025 (Sep. 2020).
[3] Z. Ni, Y.-Q. Shi, N. Ansari, and W. Su, “Reversible data hiding,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), Bangkok, Thailand, vol. 2, pp. II-912–II-915 (May 2003), doi: 10.1109/ISCAS.2003.1206326.
[4] W. Hu, X. Hu, S. Li, J. Qin, and B. Wang, “High-capacity reversible data hiding based on prediction error histogram shifting and data compression,” Multimedia Tools Appl., vol. 81, no. 10, pp. 13201–13224 (2022), doi: 10.1007/s11042-022-12154-7.
[5] Z. Yin, Y. Peng, and Y. Xiang, “Reversible data hiding in encrypted images based on pixel prediction and bit-plane compression,” arXiv preprint arXiv:1912.02945 (Dec. 2019).
[6] J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890–896 (Aug. 2003), doi: 10.1109/TCSVT.2003.815962.
[7] D. M. Thodi and J. J. Rodríguez, “Expansion embedding techniques for reversible watermarking,” IEEE Trans. Image Process., vol. 16, no. 3, pp. 721–730 (Mar. 2007), doi: 10.1109/TIP.2006.891046.
[8] W. Hong, T.-S. Chen, and H.-Y. Wu, “Reversible data hiding for high-quality images using modification of prediction errors,” J. Syst. Softw., vol. 82, no. 11, pp. 1833–1842 (2009), doi: 10.1016/j.jss.2009.05.062.
[9] Y. Qiu, X. Zhang, H. Hu, and Z. Qian, “Improved CNN prediction based reversible data hiding,” arXiv preprint arXiv:2301.01420 (2023).
[10] W. Wang and W. Wang, “HS-based reversible data hiding scheme using median prediction error,” Multimedia Tools Appl., vol. 79, no. 25–26, pp. 18143–18165 (2020), doi: 10.1007/s11042-020-08734-8.
[11] G. R. Y. Naik, N. R. Shetty, and V. K. B. Vidyasagar, “A study and analysis of reversible data hiding techniques,” in Proc. 2024 2nd Int. Conf. Adv. Inf. Technol. (ICAIT), Chikkamagaluru, India, pp. 1–6 (2024), doi: 10.1109/ICAIT61638.2024.10690366.
[12] R. Kumar, D. Sharma, A. Dua, and K.-H. Jung, “A review of different prediction methods for reversible data hiding,” J. Inf. Secur. Appl., vol. 78, Art. no. 103572 (2023), doi: 10.1016/j.jisa.2023.103572.
[13] W. Hong, S.-Y. Chen, and H.-Y. Wu, “A colour image authentication scheme with grayscale invariance,” IEEE Access, vol. 9, pp. 6522–6535 (2021), doi: 10.1109/ACCESS.2020.3048402.
[14] G. Li, S. Li, M. Li, X. Zhang, and Z. Qian, “Steganography of steganographic networks,” in Proc. AAAI Conf. Artif. Intell., vol. 37, no. 4, pp. 5178–5186 (2023), doi: 10.1609/aaai.v37i4.25646.
[15] P. Bedi and R. Dhadich, “Performance evaluation of LSB, DCT and DWT for digital image steganography,” Int. J. Comput. Appl., vol. 104, no. 2, pp. 1–6 (2014), doi: 10.5120/18195-9323.
[16] P. Bagane, S. Patil, A. Chougule, A. Patil, S. Jadhav, and S. Patil, “CyberCraft: Encryption and decryption tool for images using AES, DES, and chaotic logistic map,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-B, pp. 797–806 (2026), doi: 10.47974/JDMSC-2535.
[17] S. Gambhire, P. Bagane, S. Patil, A. Chougule, A. Patil, and S. Jadhav, “Leveraging formal languages and automata theory in natural language processing (NLP),” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-B, pp. 995–1004 (2026), doi: 10.47974/JDMSC-2552.
[18] A. K. Saini, R. Bhatnagar, and D. K. Srivastava, “AI based automatic detection of citrus fruit and leaves diseases using deep neural network model,” J. Discrete Math. Sci. Cryptogr., vol. 24, no. 8, pp. 2181–2193 (2021), doi: 10.1080/09720529.2021.1958810.
[19] G. Abdelhady, A. Mohsen, and N. Abdelnaser, “Hybrid CNN models for suspect facial recognition system,” J. Inf. Optim. Sci., vol. 47, no. 2, pp. 539–551 (2026), doi: 10.47974/JIOS-2505.
