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Abstrakty
In today’s world, the transmission of secured and noiseless images is a difficult task. Therefore, effective strategies are important for securing data or secret images from attackers. Besides, denoising approaches are important for obtaining noise-free images. For this, an effective crypto-steganography method that is based on a lightweight encryption algorithm (LEA) and the modified least significant bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based whale optimization algorithm (WOA) is used for image denoising. Before the image transmission, a secret image is encrypted by the LEA algorithm and embedded into the cover image using discrete wavelet transform (DWT) and MLSB techniques. After the image transmission, an extraction process is performed in order to recover the secret image. Finally, a bilateral WOA filter is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique results in performance that is superior to conventional bilateral and Gaussian filters in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
Wydawca
Czasopismo
Rocznik
Tom
Strony
451--466
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
autor
- KLE College of Engineering and Technology Chikodi, India
autor
- CSE and Principal, KORM College of Engineering, Tadigotla, KADAPA, Andhra Pradesh, India
autor
- Agadi College of Engineering and Technology, Department of CS&E Smt. Kamala and Sri.Venkappa M., Lakshmeshwar, Dist. Gadag, Karnataka, India
Bibliografia
- [1] Abdullah H.A., Abdullah H.N.: FPGA implementation of color image encryption using a new chaotic map, Indonesian Journal of Electrical Engineering and Computer Science, vol. 13(1), pp. 129–137, 2019.
- [2] Alabaichi A., Al-Dabbas M.A.A.K., Salih A.: Image steganography using least significant bit and secret map techniques, International Journal of Electrical & Computer Engineering (2088-8708), vol. 10(1), 2020.
- [3] Ansari A.S., Mohammadi M.S., Parvez M.T.: A multiple-format steganography algorithm for color images, IEEE Access, vol. 8, pp. 83926–83939, 2020.
- [4] Bhargava G.U., Gangadharan S.V.: FPGA implementation of modified recursive box filter-based fast bilateral filter for image denoising, Circuits, Systems, and Signal Processing, vol. 40(3), pp. 1438–1457, 2021.
- [5] Hafsa A., Gafsi M., Malek J., Machhout M.: FPGA Implementation of Improved Security Approach for Medical Image Encryption and Decryption, Scientific Programming, vol. 2021, 2021. doi: 10.1155/2021/6610655.
- [6] Ismail S.M., Ghidan A.M., Zaki P.W.: Novel chaotic random memory indexing steganography on FPGA, AEU – International Journal of Electronics and Communications, vol. 125, 2020.
- [7] Jang S.J., Hwang Y.: Noise-Aware and Light-Weight VLSI Design of Bilateral Filter for Robust and Fast Image Denoising in Mobile Systems, Sensors, vol. 20(17), 2020.
- [8] Kumar S., Jha R.K.: An FPGA-based design for a real-time image denoising using approximated fractional integrator, Multidimensional Systems and Signal Processing, vol. 31(4), pp. 1317–1339, 2020.
- [9] Lien C.Y., Tang C.H., Chen P.Y., Kuo Y.T., Deng Y.L.: A low-cost VLSI architecture of the bilateral filter for real-time image denoising, IEEE Access, vol. 8, pp. 64278–64283, 2020.
- [10] Madhusudhan K., Sakthivel P.: A secure medical image transmission algorithm based on binary bits and Arnold map, Journal of Ambient Intelligence and Humanized Computing, vol. 12(5), pp. 5413–5420, 2021.
- [11] Malladi S.R.S.P., Ram S., Rodr´ıguez J.J.: Image Denoising Using SuperpixelBased PCA, IEEE Transactions on Multimedia, vol. 23, pp. 2297–2309, 2020. doi: 10.1109/TMM.2020.3009502.
- [12] Manoj Kumar T., Karthigaikumar P.: FPGA implementation of an optimized key expansion module of AES algorithm for secure transmission of personal ECG signals, Design Automation for Embedded Systems, vol. 22(1), pp. 13–24, 2018.
- [13] Marwan M., Dos Santos V., Abidin M.Z., Xiong A.: Coexisting Attractor in a Gyrostat Chaotic System via Basin of Attraction and Synchronization of Two Nonidentical Mechanical Systems, Mathematics, vol. 10(11), 2022.
- [14] Phadikar A., Maity G.K., Chiu T.L., Mandal H.: FPGA implementation of lifting-based data hiding scheme for efficient quality access control of images, Circuits, Systems, and Signal Processing, vol. 38(2), pp. 847–873, 2019.
- [15] Prajapati P.H., Darji A.D.: FPGA implementation of MRMN with step-size scaler adaptive filter for impulsive noise reduction, Circuits, Systems, and Signal Processing, vol. 39(7), pp. 3682–3710, 2020.
- [16] Setyono A., Setiadi D.R.I.M., Muljono M.: Dual Encryption Techniques for Secure Image Transmission, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 10(3-2), pp. 41–46, 2018.
- [17] Sheela C.J.J., Suganthi G.: An efficient denoising of impulse noise from MRI using adaptive switching modified decision based unsymmetric trimmed median filter, Biomedical Signal Processing and Control, vol. 55, 2020.
- [18] Shet K.S., Aswath A., Hanumantharaju M., Gao X.Z.: Novel high-speed reconfigurable FPGA architectures for EMD-based image steganography, Multimedia Tools and Applications, vol. 78(13), pp. 18309–18338, 2019.
- [19] Shukla A.K., Pandey R.K., Yadav S., Pachori R.B.: Generalized fractional filterbased algorithm for image denoising, Circuits, Systems, and Signal Processing, vol. 39(1), pp. 363–390, 2020.
- [20] Soleimani Abhari P., Razaghian F.: A novel median based image impulse noise suppression system using spiking neurons on FPGA, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 8(6), pp. 631–640, 2020.
- [21] Taghinia Jelodari P., Parsa Kordasiabi M., Sheikhaei S., Forouzandeh B.: FPGA implementation of an adaptive window size image impulse noise suppression system, Journal of Real-Time Image Processing, vol. 16(6), pp. 2015–2026, 2019.
- [22] Varatharajan R., Vasanth K., Gunasekaran M., Priyan M., Gao X.Z.: An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images, Computers & Electrical Engineering, vol. 70, pp. 447–461, 2018.
- [23] Wang Q., Ma J., Yu S., Tan L.: Noise detection and image denoising based on fractional calculus, Chaos, Solitons & Fractals, vol. 131, 2020.
- [24] Wang S., Wang C., Xu C.: An image encryption algorithm based on a hidden attractor chaos system and the Knuth–Durstenfeld algorithm, Optics and Lasers in Engineering, vol. 128, 2020.
- [25] Yahya A.A., Tan J., Su B., Hu M., Wang Y., Liu K., Hadi A.N.: BM3D image denoising algorithm based on an adaptive filtering, Multimedia Tools and Applications, vol. 79(27), pp. 20391–20427, 2020.
- [26] Yang C.H., Chien Y.S.: FPGA Implementation and Design of a Hybrid ChaosAES Color Image Encryption Algorithm, Symmetry, vol. 12(2), 2020.
- [27] Zhang Y., Chen A., Tang Y., Dang J., Wang G.: Plaintext-related image encryption algorithm based on perceptron-like network, Information Sciences, vol. 526, pp. 180–202, 2020.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d39693ec-34c6-4671-bdd6-777c6f051f40