Deep Adversarial Extreme Learning and Mises Regression Based Secure Quality Enhanced Satellite Image Transmission
Keywords:
Compression, Bernstein–von Mises, Markovian Kernel Regression, Stochastic Gradient Generative Adversarial Network, Extreme Image CompressionAbstract
Image compression and quality enhancement are two significant applications in the domain of digital image processing. Image compression aims to minimize the number of bits necessitated for digital representation of image whereas the objective of using quality enhancement is to improve the quality of compressed images that would have been compromised during the compression process of satellite image. Also with the swift evolution of communication network, the multimedia data with image and video increases exponentially, however transmission in an efficient and secure manner has become a paramount research topic. With the intent of improving security and enhancing the image quality during transmission, we propose a method called, Deep Gradient Adversarial Extreme Learning and MisesMarkovian Regression quality-enhanced secure image transmission (DGAEL-MMR) is proposed. The DGAEL-MMR method is split into two sections, namely, image compression and quality enhancement for secure transmission. First with the satellite images provided as input, Deep learning model called, Stochastic Gradient Generative Adversarial Network and Extreme Image Compression is proposed to ensure the security of satellite images by maintaining their confidentiality and integrity. Second to enhance the quality of compressed satellite images, Bernstein–von MisesMarkovian Kernel Regression Optimization-based model is applied. This proposed DGAEL-MMR method aims to boost the security of satellite images during their transmission across networks. Quality enhancement parameters like Peak Signal to Noise Ratio (PSNR), Bit Error Rate (BER) and security parameters like confidentiality and integrity are used to assess and compare the performances of the method. Obtained results show that the DGAEL-MMR method performs better than the conventional methods both in terms of the security (i.e., data confidentiality by 16% and data integrity by 20%) parameters and quality enhancement (i.e., PSNR by 26%) mentioned above.
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S. Fraihat, M.A. Al-Betar. A novel lossy image compression algorithm using multi-models stacked AutoEncoders. Array. 2023; 19:100314.
M.B. Begum, N. Deepa, M. Uddin, R. Kaluri, M. Abdelhaq, R. Alsaqour. An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm. Heliyon. 2023; 9(6).
H.R. Latha, A. Ramaprasath. HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme. Journal of Intelligent Systems. 2023; 32(1):20229056.
Q. Chen, Y. Wu, X. Wang, Z.L. Jiang, W. Zhang, Y. Liu, M. Alazab. A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023; 16:3309-21.
G. Dax, S. Nagarajan, H. Li, M. Werner. Compression supports spatial deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022; 16:702-13.
I. Shiri, B. Razeghi, S. Ferdowsi, Y. Salimi, D. Gündüz, D. Teodoro, S. Voloshynovskiy, H. Zaidi. PRIMIS: Privacy-preserving medical image sharing via deep sparsifying transform learning with obfuscation. Journal of biomedical informatics. 2024; 150:104583.
A. Gaudio, A. Smailagic, C. Faloutsos, S. Mohan, E. Johnson, Y. Liu, P. Costa, A. Campilho. DeepFixCX: Explainable privacy‐preserving image compression for medical image analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2023; 13(4):e1495.
R. Archana, P.E. Jeevaraj. Deep learning models for digital image processing: a review. Artificial Intelligence Review. 2024; 57(1):11.
S.D. Isaac, Z.T. Njitacke, N. Tsafack, C.T. Tchapga, J. Kengne. Novel compressive sensing image encryption using the dynamics of an adjustable gradient Hopfield neural network. The European Physical Journal Special Topics. 2022; 231(10):1995-2016.
S. Gayathri, S. Gowri. Securing medical image privacy in cloud using deep learning network. Journal of Cloud Computing. 2023; 12(1):40.
M. Khairy, R.M. Al-Makhlasawy. A reliable image compression algorithm based on block luminance adopting deep learning for video surveillance application. Journal of Electrical Systems and Information Technology. 2022; 9(1):21.
B.S. Kumar, R. Revathi. An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network. Journal of Engineering and Applied Science. 2024; 71(1):41.
A. Almalawi, S. Hassan, A. Fahad, A.I. Khan. A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks. International Journal of Computational Intelligence Systems. 2024; 17(1):24.
Y. Chen, T. Zhou, J. Wu, H. Qiao, X. Lin, L. Fang, Q. Dai. Photonic unsupervised learning variationalautoencoder for high-throughput and low-latency image transmission. Science Advances. 2023; 9(7):eadf8437.
M.U. Sana, Z. Li, T. Kiren, H.B. Liaqat, S. Naseem, A. Saeed. A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing. Computers, Materials & Continua. 2023; 75(2).
V. Barot, R. Patel. A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications. Biomedical Signal Processing and Control. 2022; 73:103436.
E. Suel, S. Bhatt, M. Brauer, S. Flaxman, M. Ezzati. Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas. Remote Sensing of Environment. 2021; 257:112339.
K.M. Hosny, A.M. Khalid, H.M. Hamza, S. Mirjalili. Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function. Neural Computing and Applications. 2023; 35(1):855-86.
M.R. Khosravi, P. Tavallali. Real-time statistical image and video processing for remote sensing and surveillance applications. Journal of Real-Time Image Processing. 2021; 18:1435-9.
D. Mishra, O. Hadar. Self-FuseNet: data free unsupervised remote sensing image super-resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023; 16:1710-27.
L. Sharma, S. Sengupta, B. Kumar. An improved technique for enhancement of satellite image. InJournal of Physics: Conference Series 2021 (Vol. 1714, No. 1, p. 012051). IOP Publishing.
F. Xu, Y. Shi, P. Ebel, L. Yu, G.S. Xia, W. Yang, X.X. Zhu. GLF-CR: SAR-enhanced cloud removal with global–local fusion. ISPRS Journal of Photogrammetry and Remote Sensing. 2022; 192:268-78.
V. Vasilescu, M. Datcu, D. Faur. A CNN-based Sentinel-2 image super-resolution method using multiobjective training. IEEE Transactions on Geoscience and Remote Sensing. 2023; 61:1-4.
P. Gallés, K. Takáts, M. Hernández-Cabronero, D. Berga, L. Pega, L. Riordan-Chen, C. Garcia, G. Becker, A Garriga, Bukva, J. Serra-Sagristà. A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as a Proxy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023.
R. Ablin, C.H. Sulochana, G. Prabin. An investigation in satellite images based on image enhancement techniques. European Journal of Remote Sensing. 2020; 53(sup2):86-94.
X. Zhang, Y. Ren, G. Zhen, Y. Shan, C. Chu. A color image contrast enhancement method based on improved PSO. Plos one. 2023; 18(2):e0274054.
P.M.K. Prasad, Y. RaghavenderRao, Karri Chiranjeevi, “Contrast Enhancement Of Satellite Images Based On Lifting Haar Wavelet Transform And Singular Value Decomposition”, International Journal of Emerging Trends in Engineering Research, 2020
T.A. Bui, P.J. Lee, C.S. Liang, P.H. Hsu, S.H. Shiu, C.K. Tsai. Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024.
D. Żelasko, W. Książek, P. Pławiak. Transmission quality classification with use of fusion of neural network and genetic algorithm in Pay&Require multi-agent managed network. Sensors. 2021; 21(12):4090.
Z. Saberi, N. Hashim, A. Ali, P. Boursier, J. Abdullah, Z.C. Embi. Adaptive contrast enhancement of satellite images based on histogram and non-linear transfer function methods. IAENG International Journal of Applied Mathematics. 2023; 53(1):1-9.
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