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Go to Editorial ManagerIn nowadays world of rapid evolution of exchanging digital data, data protection is required to protect data from the unauthorized parities. With the widely use of digital images of diverse fields, it is important to conserve the confidentiality of image’s data form any without authorization access. In this paper the problem of secret key exchanging with the communicated parities had been solved by using a random number generator which based on Linear Feedback Shift Register (LFSR). The encryption/decryption is based on Advance Encryption Standard (AES) with the random key generator. Also, in this paper, both grayscale and colored RGB images have been encrypted/decrypted. The functionality of proposed system of this paper, is concerned with three features: First feature, is dealing with the obstetrics of truly random and secure encryption key while the second one deals with encrypting the plain or secret image using AES algorithm and the third concern is the extraction the original image by decrypting the encrypted or cipher one. “Mean Square Error (MSE)”, “Peak Signal to Noise Ratio (PSNR)”, “Normalized Correlation (NK)”, and “Normalized Absolute Error (NAE)” are measured for both (original-encrypted) images and (original-decrypted) image in order to study and analyze the performance of the proposed system according to image quality features.
With the rapid development of multimedia technology, securing the transfer of images becomes an urgent matter. Therefore, designing a high-speed/secure system for color images is a real challenge. A nine-dimensional (9D) chaotic- based digital/optical encryption schem is proposed for double-color images in this paper. The scheme consists of cascaded digital and optical encryption parts. The nine chaotic sequences are grouped into three sets, where each set is responsible for encryption one of the RGB channels independently. One of them controls the fusion, XOR operation, and scrambling-based digital part. The other two sets are used for controlling the optical part by constructing two independent chaotic phase masks in the optical Fourier transforms domain. A denoising convolution neural network (DnCNN) is designed to enhance the robustness of the decrypted images against the Gaussian noise. The simulation results prove the robustness of the proposed scheme as the entropy factor reaches an average of 7.997 for the encrypted color lena-baboon images with an infinite peak signal-to-noise ratio (PSNR) for the decrypted images. The designed DnCNN operates efficiently with the proposed encryption scheme as it enhances the performance against the Gaussian noise, where the PSNR of the decrypted Lena image is enhanced from 27.01 dB to 32.56 dB after applying the DnCNN.
In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.
According to the characteristic of HVS (Human Visual System) and the association memory ability of neural network, an adaptive image watermarking algorithm based on neural network is proposed invisible image watermarking is secret embedding scheme for hiding of secret image into cover image file and the purpose of invisible watermarking is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits of the two procedures. The IWT have impediment of portion misfortune in embedding which increments mean square estimate as SIM and results diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling and Hopfield neural network. The proposed algorithm presents hybrid integer wavelet transform and discrete cosine transform based watermarking technique to obtain increased imperceptibility and robustness compared to IWT-DCT based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT based watermarking.
The security of communications in various transmitted information’s forms such as video, audio, image, and even text and preserving them from attackers has become of great importance in the age of the Internet and cellular networks. Perhaps one of the most important media used to transmit information is digital images. They are distinguished from video and audio by their lack of complexity, and at the same time they are distinguished from text by the possibility of containing more information. Due to the necessity of transmitting huge amounts of information via digital images through additive white Gaussian noise (AWGN) channels for various applications. This transmission of images over unsecured channels is vulnerable to many attacks that must be protected by information security tools. In this research, a hybrid chaos-based system was developed to encrypt and secure images and send them via an orthogonal frequency division multiplexing (OFDM) channel, which leads to transferring large amounts of transmitted information in a short time, with very little interference between the data, and maintaining the transfer rate. Two chaotic techniques, Rossler and Modified Chau system, are used together to create a secret encryption key. This combination of chaotic systems provides highly random sensitive keys with amplitude of 10252 that are difficult to predict by the attacker and which makes restoring the original image very difficult in the event of a very small change in the chaotic parameters. Many tests were conducted to determine the strength of the proposed system, including statistical and differential analysis and entropy to verify the strength of the image security approach, in addition to applying some types of attacks to the encrypted image, such as noise and cropping different parts of the image. It is clear that the proposed scheme has strong immunity to these attacks. This was proven by the comparative experimental results. The entropy ratio was very excellent compared to the rest of the results obtained in the rest of the research. This was also the case with the values of (NPCR), (UACI), (NPCR), and Mean Square Error (MSE) was also very good as compared with other researches in the literature. The proposed security approach for OFDM gave a low link and a low bit error rate. And a higher signal-to-noise ratio (PSNR).
This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities.
Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M).