Multi key algorithm for performance enhancement of video encryption

This paper contrasts the implementation of several chaos-based digital image watermarking techniques in terms of imperceptibility, robustness, security and complexity. Most spatial and transform domains methods have been used namely Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Redundant Discrete Wavelet Transform (RDWT) with Singular Value Decomposition (SVD) mathematical tool which has enhanced embedding capability. Both continuous and discrete chaotic maps namely Arnold's Chaotic Map (ACM), Logistic Chaotic map (LCM) and Lorenz Chaotic Map (LoCM) are included to result in secure schemes. Exhaustive series of tests have been performed along with a range of evaluation metrics for imperceptibility evaluation. In addition, Robustness of all the techniques has also been confirmed by applying a variety of attacks on the resulting images. This study will help to adopt the most suitable methods for a given application as sufficient number of test images have been utilized.

Chaotic map gained its importance in the field of cryptography, due to its properties like, randomness, unpredictability, sensitivity on initial condition, aperiodicity, which is used to generate pseudorandom bit streams. In this paper the optimal values of chaos parameters are generated through Real Coded Genetic Algorithm (RCGA), which is optimal, unpredictable, and optimally sensitive. Here, a non-deterministic RCGA based optimal pseudo-random bit sequence generator based on Chaotic maps, such as Logistic Chaotic map, Skew Tent Chaotic map, Cross Coupled Logistic Chaotic map, Cross Coupled Skew Tent Chaotic map is proposed. A real coded crossover and mutation technique is proposed for RCGA. Seed values for chaotic map have been optimized by using all sub-functions of GA (RCGA). These seed values are used to generate optimal pseudorandom bit stream of finite length. The randomness of the bit stream is tested by using NIST statistical test suit.

This paper experimentally investigates the effect of nine chaotic maps on the performance of two Particle Swarm Optimization (PSO) variants, namely, Random Inertia Weight PSO (RIW‐PSO) and Linear Decreasing Inertia Weight PSO (LDIW‐PSO) algorithms. The applications of logistic chaotic map by researchers to these variants have led to Chaotic Random Inertia Weight PSO (CRIW‐PSO) and Chaotic Linear Decreasing Inertia Weight PSO (CDIW‐PSO) with improved optimizing capability due to better global search mobility. However, there are many other chaotic maps in literature which could perhaps enhance the performances of RIW‐PSO and LDIW‐PSO more than logistic map. Some benchmark mathematical problems well‐studied in literature were used to verify the performances of RIW‐PSO and LDIW‐PSO variants using the nine chaotic maps in comparison with logistic chaotic map. Results show that the performances of these two variants were improved more by many of the chaotic maps than by logistic map in many of the test problems. The best performance, in terms of function evaluations, was obtained by the two variants using Intermittency chaotic map. Results in this paper provide a platform for informative decision making when selecting chaotic maps to be used in the inertia weight formula of LDIW‐PSO and RIW‐PSO.

Watermarking has become a crucial technique for protecting copyright and verifying the authenticity of images due to the rapid distribution of digital images and the ease and reliability of image content modification. In this research, we introduce a hybrid digital picture watermarking method that is both secure and resilient. This would use the discrete wavelet transform (DWT), the discrete cosine transforms (DCT), singular value decomposition (SVD), and cryptographic systems such as RSA and chaotic maps. Hence, this method tries to balance the tradeoff of robustness, security, and imperceptibility. We first encrypt the watermark using RSA, and then a chaotic logistic map (CLM) scrambles the already designed encrypted watermark to further enhance its robustness. By putting the watermark in the host image's separately calculated DWT-DCT coefficients, the proposed method works well even when compression, noise addition filtering, and geometric attacks are used. The test results demonstrate that the suggested method does better than other methods in Structural Similarity Index (SSIM), Normalized Correlation (NC), and Peak Signal-to-Noise Ratio (PSNR). Regardless of the nature of the attack, our proposed method consistently generates PSNR values above 55 dB and NC values above to 0.90, indicating correct watermark extraction and excellent image perception. We use cryptographic methods to secure the watermark from retrieval and modification by unauthenticated users. In addition, our approach is superior to the current watermarking methods in robustness and unobtrusiveness. In further works, we plan to improve the robustness of the approach against more complex geometric attacks and apply it for watermarking video and 3D content.

The main purpose of the proposed image encryption (IE) method is to enhance various security characteristics to protect sensitive images on the internet. In the literature, chaotic maps are preferred for image encryption because of their high randomness and sensitivity to initial parameter values. Therefore, metaheuristic algorithms have been utilized to fine-tune the parameters. In this research, we have employed the metaheuristic Lyrebird optimization (LO) algorithm to fine-tune the chaotic map parameters. The LO algorithm is chosen over other algorithms due to its better exploration and exploitation rate to search for the best solution. The LO algorithm searches for the best parameter value based on the objective function. This research involves the formulation of a multi-objective function that incorporates numerous security parameters, namely, entropy and correlation coefficient (CC), to improve the security attributes of the IE model. The proposed IE model has two steps. In the first step, a random key is generated to transform the pixels of the secret image, whereas in the second step, the image pixels are shuffled based on the shuffling index values to reduce the correlation among the neighboring pixels. In both steps, we have used the chaotic logistic map. The proposed IE is evaluated against statistical and differential attacks on the standard dataset by considering the various images and their different resolutions. The uniform distribution of the histogram of the encrypted images is validated using the chi-square test. Besides that, entropy analysis shows that the average entropy value is 7.9973, 7.9993, and 7.9998 for 256x256, 512x512, and 1024x1024 image resolutions. The average CC and SSIM are near zero value. The average NPCR value (>0.9969) is near the 1 value. Finally, the comparative analysis based on entropy, CC, and NPCR parameters shows that the proposed IE model outperforms the existing models.

In this research study, a multi-purpose image water-marking approach was developed for delivering selfrecovery, ownership verification and tamper localization of the cover image. For robust watermarking, color and grayscale images were used to deliver wide applicability and generalization to proposed work. Initially, the cover image was converted into wavelet domain by using Integer Wavelet Transform (IWT) and then the logo image was encrypted by using chaotic logistic mapping. This procedure makes the digital image free from false positive error and also helps to provide decent capacity. After encryption and transformation, Least Significant Bit (LSB) was used to provide self-recovery feature as well as to locate the tempered region in the digital image. Then, asymmetric encryption algorithm: Rivest-Shamir-Adleman (RSA) was utilized to improve the hidden data security with high computational efficiency, and good embedding capacity. Finally, the experimental outcome shows that the proposed approach delivers a high secure network with low computational complexity by means of entropy value and Peak Signal-to-Noise Ratio (PSNR). The proposed approach improved PSNR value upto 19 dB and entropy value upto 0.07-0.7 compared to the existing methods: Discrete Cosine Transform (DCT) + Arnold transform and differential evolution algorithm.

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.

Chaotic maps have gained popularity in image encryption models due to their ability to secure images with a random key.Further, the randomness of the key is highly dependent on the initial parameter values of the chaotic map.Therefore, the literature employs metaheuristic algorithms to find the optimal parameter values for generating a completely random key.Two chaotic maps-the 1-D chaotic logistic map (CLM) and the chaotic tent map (CTM)-along with the metaheuristic algorithm known as one-to-one-based optimization (OOBO) were used to create an optimized image encryption (IE) model in this study.We utilize the 1-D CLM algorithm for random key generation, while the 1-D CTM algorithm generates the shuffling sequence for image shuffling.We use the metaheuristic OOBO algorithm in both chaotic maps to generate completely random keys and random shuffling sequences based on the objective function.In this research, we design the objective function using security parameters such as entropy (E) and correlation coefficient (CC).After determining the optimal parameter values for chaotic maps, we perform an exclusive-OR operation between the random key and the secret image.After that, shuffling of the secret image is done based on the shuffling sequence given by the 1-D CTM algorithm.We evaluate the proposed IE model through simulation using images from the primary and secondary datasets.Using various parameters, we compare the visual quality of the encrypted image with the secret image in this evaluation.The results illustrate that the proposed IE model outperforms in terms of entropy and correlation coefficients and provides better encryption than the existing IE models.The proposed model achieves an entropy of 7.99648 for the cameraman, 7.99688 for Elaine, and 7.99704 for the pepper image.Further, a correlation coefficient of 0.00021 for the cameraman, -0.00012 for Elaine, and -0.00161 for the pepper image.

The study provides a unique method for creating an efficient substitution box (S-box) for advanced encryption standards using a Chaotic Logistic Map (CLM) and a Linear Congruential Generator (LCG) (AES) for secure communications in a smart city. The Pseudo-Random Number Generator (PRNG), which is further examined, is constructed using an extensive search of reasonable possibilities for the initial seed and set parameters. Using statistical testing, the performance analysis of the new S-box is assessed. Additionally, the resilience of differential, as well as linear cryptanalysis, is shown. It is derived using other features, including nonlinearity, the Bit Independence Criterion (BIC), and the Strict Avalanche Criterion (SAC). The suggested S-box has good potential and is usable for symmetric key cryptography, according to the features of the new S-cryptographic box.

A simplified and efficient technique of cryptography of an image will be developed. The technique will utilize two simple rounds to apply image encryption and decryption, each round will be implemented by performing a simple rows shuffling and columns shuffling, eliminating the need for computational logical and arithmetic operations required for other techniques of image encryption. Our approach, while simplifying computational demands by eliminating complex arithmetic and logical operations, maintains robust security through the integration of dual Chaotic Logistic Map Models (CLMM) to generate highly sensitive indices keys. These indices keys control row and column shuffling, which provides a high degree of permutation and resists known-plaintext and cipher text-only attacks. The technique will require two secret indices keys, these keys will be variable in the length and the contents and they will depend on the image to be encrypted-decrypted, generation of these keys will be very easy and simple. The shuffling operations will be implemented using two secret indices keys, and by implementing couple of chaotic logistic map these keys will be produced using selected chaotic parameters values included in the private key. The technique uses a 256-bit private key, chaotic logistic maps, and high sensitivity to parameter variations to achieve "Very strong" key space entropy, with encryption throughput of 19,540 KB/s and robust performance across diverse image sizes. The generated decrypted image may become so sensitive to chosen of private key values, and any little variation in the private key through the decryption task, it will be showed as a hacking try. The proposed technique will be flexible, it will be used to process every image with any size (gray and/or color), and image changing will not require any updating in the decryption and encryption processes. The developed technique will be efficient; it will decrease both the encryption and decryption times, and it lead to accelerate the image encryption process comparing with other techniques. The study systematically utilizes specific evaluation metrics to substantiate claims regarding the quality, security, and velocity of the proposed cryptographic technique. Quality is assessed by Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics

The usage of multimedia data has grown exponentially over the past few decades, with a significant portion of its content in the form of images and videos, which are transmitted across various public networks. As the prevalence of video content continues to rise, so does the critical need for enhanced data security. Recently, several techniques have been proposed to create efficient data security procedures, focusing on security, robustness and computational complexity. This paper proposes a secure and robust video encryption technique based on the concepts of confusion and diffusion. During the confusion phase, horizontal and vertical permutations of pixel values are performed using a permutation box and then pixel values are substituted based on a lookup table. In the diffusion phase, the pixel values of the first frame are XORed with a defined box, and then, sequentially, the pixel values in the remaining frames are XORed using the previous frame as a reference. Each stage requires keys for processing that are generated by three different chaotic logistic maps, which successfully passed the NIST test. These numbers are then XORed to implement a multi‐chaotic logistic map effectively. Simulation metrics, including entropy, correlation, NPCR, UACI, and so forth demonstrate the strength of the proposed method. The result analysis also states that the proposed methodology performs well in terms of Quantitative Security Analysis Correlation Analysis 0.0017, Entropy Analysis 7.99, Peak Signal to Noise Ratio 7.898, Mean Square Error 10549.37, Structural Similarity Index (0.0075), Key Space Analysis (), Differential Attack Analysis (Number of Pixel Change Rate 99.61 and Unified Average Change Intensity 33.46), Robustness Analysis (Salt and Paper Noise (pass), Gaussian Noise (pass), Occlusion Attack (pass) and Execution Time Analysis θ ( n )).

As a meta-heuristic algorithm based on swarm intelligence, the WOA algorithm has few control parameters and searches for the optimal solution by encircling the prey, searching for the prey, and attacking the bubble net. During the whole process, only two internal parameters A and C are utilized for the control of the exploration and development process. BWOA is simple to implement. In the process of algorithm execution, the initial population, global exploration, and local development stages have shortcomings. Therefore, it is necessary to optimize the WOA algorithm. Based on WOA, this study conducts a high-performance computing analysis and location selection of logistics distribution center space. It is concluded that: (1) by using the combination of direct logistics distribution and hierarchical logistics distribution, the WOA algorithm optimizes the cross selection strategy, the population fitness S-LO is improved, the quality of LA is guaranteed, and the chaotic S-LO mapping eliminates inferior individuals in the population. Direct distribution is carried out for bulky goods and important distribution customers, and hierarchical logistics distribution is used for customers in intensive logistics distribution destinations. (2) WOA uses the second reverse learning, chaotic mapping, and logistic chaotic mapping to improve the location update mode. The direct distribution method is mostly used for the logistics business with short journeys, fixed distribution points, and more goods delivered at one time, and logistics enterprises do not need to store and distribute goods. The uniform ergodicity of the Tent chaotic map and logistic chaotic map is improved. Ka adaptive inertia weights are a good complement to optimize the limitations of the Ao whale algorithm. (3) The inertia weight of the levy flight behavior can play a powerful role in balancing the global exploration ability and optimization performance of the intelligent algorithm. The long-term short-distance search of HED and the long-distance jump of KVAR are combined. Variant individuals undergo vector synthesis. It reduces the construction and operation costs of logistics sites and is suitable for logistics distribution under specific conditions.

It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor gamma and kernel parameter sigma for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.

Steganography has gained importance in various fields, including cloud security, internet banking, military applications, and medical imaging. Additionally, it is popular due to its many uses, becoming a hot research topic. The Least Significant Bit (LSB) approach is among the simplest ways to embed secret data in a cover image. We proposed a new scheme using LSB with the genetic algorithm GA to find the optimal solution permutation for embedding the pixel assortment of the image where data is to be concealed, and a chaotic genetic algorithm (CGA) to efficiently find the best crossover and mutation performance in the chromosomes. Random keys are created by using a 2D logistic map, and this key is used to encrypt gray images. So it is tough to find secret information. When comparing the PSNR, MSE, and SSIM metrics. The results of the analysis show that the recommended approach provides a useful level of security. With a focus on capacity and image quality where the 4-LSB value and baboon host image of size 512*512 with a maximum capacity up to 65k, the population size and iteration values for the genetic parameters are 50 and 50, respectively the PSNR reach 58.6, SSIM is 0.99995040 and MSE is 0.0896 Although, there is a negotiation between these metrics and keeping a better correspondence between them is still a big challenge.

In this paper, we proposed a new image encryption algorithm based on parameter-varied logistic chaotic map and dynamical algorithm. The parameter-varied logistic map can cure the weaknesses of logistic map and resist the phase space reconstruction attack. We use the parameter-varied logistic map to shuffle the plain image, and then use a dynamical algorithm to encrypt the image. We carry out several experiments, including Histogram analysis, information entropy analysis, sensitivity analysis, key space analysis, correlation analysis and computational complexity to evaluate its performances. The experiment results show that this algorithm is with high security and can be competitive for image encryption.