High-performance image encryption framework using 2D nonuniform FFT and TDES for securing cognitive applications

The importance of image encryption has considerably increased, especially after the dramatic evolution of the internet and network communications, due to the simplicity of capturing and transferring digital images. Although there are several encryption approaches, chaos-based image encryption is considered the most appropriate approach for image applications because of its sensitivity to initial conditions and control parameters. Confusion and diffusion methods have been used in conventional image encryption methods, but the ideal encrypted image has not yet been achieved. This research aims to generate an encrypted image free of statistical information to make cryptanalysis infeasible. Additionally, the motivation behind this work lies in addressing the shortcomings of conventional image encryption methods, which have not yet achieved the ideal encrypted image. The proposed framework aims to overcome these challenges by introducing a new method, Equal Pixel Values Quantization (EPVQ), along with enhancing the confusion and diffusion processes using chaotic maps and additive white Gaussian noise. Key security, statistical properties of encrypted images, and withstanding differential attacks are the most important issues in the field of image encryption. Therefore, a new method, Equal Pixel Values Quantization (EPVQ), was introduced in this study in addition to the proposed confusion and diffusion methods to achieve an ideal image encryption framework. Generally, the confusion method uses Sensitive Logistic Map (SLM), Henon Map, and additive white Gaussian noise to generate random numbers for use in the pixel permutation method. However, the diffusion method uses the Extended Bernoulli Map (EBM), Tinkerbell, Burgers, and Ricker maps to generate the random matrix. Internal Interaction between Image Pixels (IIIP) was used to implement the XOR (Exclusive OR) operator between the random matrix and scrambled image. Basically, the EPVQ method was used to idealize the histogram and information entropy of the ciphered image. The correlation between adjacent pixels was minimized to have a very small value (×10−3). Besides, the key space was extended to be very large (2450) considering the key sensitivity to hinder brute force attacks. Finally, a histogram was idealized to be perfectly equal in all occurrences, and the resulting information entropy was equal to the ideal value (8), which means that the resulting encrypted image is free of statistical properties in terms of the histogram and information entropy. Based on the findings, the high randomness of the generated random sequences of the proposed confusion and diffusion methods is capable of producing a robust image encryption framework against all types of cryptanalysis attacks.

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. The main advantages of this work are (1) vast key space and (2) high encryption efficiency. The superior key space of 22551 is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of using the SNM as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite. Average achieved values for the metrics include MSE of 9626, PSNR of 8.3 dB, MAE of 80.99, entropy of 7.999, NPCR of 99.6% and UACI of 31.49%.

A novel image encryption framework is proposed in this article. A new chaotic map and a pseudorandom bit generator are proposed. Apart from this, a novel image encryption system is designed based on the proposed map and the proposed pseudorandom bit generator. These three are the major contributions of this work that makes a complete cryptosystem. The proposed new chaotic map is proposed which will be known as the ‘RCM map’ and its chaotic property is studied based on Devaney’s theory. The proposed pseudorandom bit generator is tested using the NIST test suite. The proposed method is simple to implement and does not involve any highly complex operations. Moreover, the proposed method is completely lossless, and therefore cent percent of data can be recovered from the encrypted image. The decryption process is also simple to implement i.e. just reverse of the encryption procedure. A scrambling algorithm is also proposed to further enhance the security of the overall system. The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it. The experimental results are very promising and show the prospects of chaos theory and its usage in the field of data security.

WITHDRAWN: The generalized non-linear fresnel transform and its application to image encryption

Image encryption plays a vital role in protecting digital data from unauthorized access, cyber threats, and security breaches. With the continuous evolution of attack strategies, developing highly secure encryption mechanisms becomes essential. This paper presents a novel image encryption framework that integrates Cellular Automata (CA) with a chaotic map to enhance image confidentiality and integrity. The spatial complexity of 2-D CA strengthens the key generation process, ensuring a high degree of randomness in the key image. Subsequently, a 3-D coupled logistic map enhances chaotic behavior during encryption, increasing system complexity and security. To further ensure data integrity, Reed-Solomon codes are incorporated into both the key and the encrypted image, improving resilience against transmission errors. The proposed scheme undergoes rigorous evaluation through multiple analyses, achieving high entropy values nearing 7.9989, indicating enhanced encryption randomness. Additionally, it attains an average UACI of 36.9223 and an NPCR of 99.7887, demonstrating strong resistance against differential attacks. Furthermore, the model achieves an average MSE of 88.058 and a PSNR of 29.594 dB, ensuring high-quality image reconstruction. The encryption’s robustness is further confirmed through the NIST randomness test suite, where the model achieves an average p-value of 0.6771 across 15 tests, validating the statistical randomness of the generated cipher images. Moreover, the scheme exhibits strong resilience against noise attacks, confirming its practical applicability in noisy transmission environments. Collectively, these results demonstrate the robustness, effectiveness, and superiority of the proposed image encryption framework.

A hybrid encryption framework based on Rubik’s cube for cancelable biometric cyber security applications

A fast synthetic aperture (SA) focusing algorithm is proposed in this article to overcome the problem of huge computational complexity in developing high-performance SA ultrasound imaging systems. The proposed method realizes the fast focusing of SA imaging by utilizing a virtual array (VA) technique in combination with nonuniform fast Fourier transform (VA-NUFFT) method. The computational performance of the VA-NUFFT algorithm is evaluated with the computational complexity under acceptable beam errors, compared with the conventional delay-and-sum (DAS) method and the frequency-domain direct method (DM), as well as a multiple NUFFT (M-NUFFT) method. Simulation results reveal that under a beam error level less than −57.4 dB, 25 and 10 reduction ratios of entire real operations can be achieved by the VA-NUFFT at larger SA scan lines of 2048, when compared with the DAS and DM, respectively. In the key SA focusing step, i.e., the “spatial process” step, the computational load of VA-NUFFT is lower than two orders of magnitude of that of the DM. SA imaging of a phantom with four cysts is also simulated and a contrast-to-noise ratio (CNR) criterion is adopted for evaluation of imaging quality. CNR results reflect that SA images of using VA-NUFFT make almost no difference to that of DAS focusing under different signal-to-noise ratios (SNRs).

The day-to-day progress in communication plays a vital role in transmitting millions and trillions of data through the unsecured network channels. It creates a way where the user’s data becomes the victim of various security threats. Among those users’ data, images act as primary data, and its encryption security methodologies are fascinating. The conventional encryption techniques don’t work well against the various other hidden security threats but require substantial computational time and cost with poor permutation performance. Hence to deal with this, an auto-encoder induced DNA (Deoxyribonucleic acid) sequence via chaotic image encryption framework is designed in our proposed work. It integrates the properties of DNA encoding and the chaotic maps to handle the data losses effectively and resist several attacks such as statistical attacks, chosen-plaintext attacks, etc. Moreover, an auto-encoder is used to control the data noises, thereby ensuring a better encryption performance. Here, the auto-encoder is activated to generate a permuted image with less time complexity and noise. A secret key is then initialized with the aid of SHA-256. Finally, image encryption and decryption are achieved, followed by the successful transmission of data over a digital network. The performance of the proposed work is analyzed with varied metrics to strengthen its efficiency over the prior techniques.

The generation, storage, and transmission of digital images have become ubiquitous in today’s interconnected world. Ensuring the security of these images is a critical challenge that demands immediate attention. This study proposes a novel image encryption algorithm designed to address these concerns effectively. The proposed framework leverages the unique properties of three key constructs: the Wireworld cellular automaton, the 1D logistic chaotic map, and the piecewise linear chaotic map. The 1D logistic chaotic map is employed to generate random numbers, which are used to initialize the Wireworld cellular automaton. The automaton, in turn, introduces scrambling effects into the plaintext image, effectively disrupting its pixel arrangement. Additionally, the piecewise linear chaotic map is utilized to achieve diffusion effects, further enhancing the security of the encryption process. Extensive security analyses and machine experiments have yielded highly promising results. The proposed algorithm has been rigorously evaluated using a variety of validation metrics, including key space analysis, correlation coefficient, Cartesian and polar histograms, information entropy, histogram variance, and peak signal-to-noise ratio (PSNR). In particular, we got an entropy of 7.9975 and histogram variance 251.9867. These metrics collectively demonstrate the algorithm’s strong security characteristics and its resilience against potential attacks. The findings suggest that the proposed image cipher is not only highly secure but also practical for real-world applications. It holds significant potential for safeguarding digital images across diverse domains, including healthcare, military, and multimedia communication. This study underscores the viability of the proposed approach as a reliable solution for ensuring the security of images in an increasingly digital world.

To resolve the finite computing precision problem in chaotic cryptography, and the security and efficiency drawbacks of deep learning-based image cryptosystems, an image encryption framework with low computing precision is presented based on three-dimensional Boolean convolution neural network (3D-BCNN). Unlike traditional CNN, the proposed 3D-BCNN is composed of only convolutional layers, in which a cross-channel 3-D Boolean convolution operation is devised without training and parameter optimization. To strengthen the security and sensitivity of the cryptosystem, the kernels and convolutional matrices are generated by the combined prime modulo multiplicative linear congruence generators, SHA-1, and a plain-image. All operations of 3D-BCNN can be conducted on devices with 8-bit word length, so the proposed encryption scheme could work well with low computing precision. Simulation results demonstrate that, with the largest computing accuracy 2−8, the proposed encryption scheme has high security and low time complexity, and can withstand various attacks.

In this paper, a novel image encryption approach is proposed in the context of cloud computing applications. A fast special transform based on non-equispaced grid technique is introduced and applied as the first time in image encryption applications. By Combining with Fractional Fourier Transform (FRFT) instead of Discrete Fourier Transform (DFT), a good framework for image encryption is opened to enhance data security degree. The both image encipherment and decipherment process are analyzed based on random phase matrix. The time complexity effort of this novel approach is examined and evaluated. Comparative study with traditional encryption algorithms will prove the efficiency and robustness of our proposed technique.

Recently, the advent of Internet of Medical Things (IoMT) has effectively alleviated the problem of difficulty in accessing medical services. However, during telemedicine, various medical images containing sensitive private information are exposed in communication channels. Therefore, there is an immediate need for an effective encryption method to ensure the secure transmission of medical images. In this paper, an image encryption algorithm is proposed based on a new chaotic model and an enhanced S-box. Furthermore, the proposed encryption algorithm is applied within a novel asymmetric image encryption framework. Firstly, to address the problems of narrow chaotic intervals and uneven trajectory distribution present in some existing chaotic maps, a two-dimensional cross-sine-modular model (2D-CSMM) is constructed. Secondly, in conjunction with the DNA algorithm, an enhanced S-box is proposed. Finally, in order to effectively protect key transmission as well as to enhance the system's resistance against chosen plaintext attacks, a novel asymmetric image encryption framework is designed by integrating the Elliptic Curve Diffie-Hellman (ECDH), Elliptic Curve Cryptography (ECC) and SHA-256 functions. And the secret key is computed from the cipher key, the shared key, and the native key associated with the plain image. Comprehensive experimental results demonstrate the high efficiency and the resilience of this new algorithm against common attacks.

Due to the growing of the use of Internet and communication media, image encryption is rapidly increased. Image sharing through unsafe open channels is vulnerable for attacking and stealing. For protecting the images from attacks, encryption techniques are required. Recently, new and efficient chaos‐based techniques have been suggested to develop secure image encryption. This study presents a novel image encryption framework based on integrating the chaotic maps and color codes. Three phases are involved in the proposed image encryption technique. Piecewise chaotic linear map (PWLCM) is used in the first phase for permuting the digital image. In the second phase, substitution is done using Hill cipher which is the mixing of color codes with the permuted image. The third phase is implemented by XORing, a sequence generated by the chaotic logistic map (CLM). The proposed approach enhances the diffusion ability of the image encryption making the encrypted images resistant to the statistical differential attacks. The results of several analyses such as information entropy, histogram correlation of adjacent pixels, unified average changing intensity (UACI), number of pixel change rate (NPCR), and peak signal‐to‐noise ratio (PSNR) guarantee the security and robustness of the proposed algorithm. The measurements show that the proposed algorithm is a noble overall solution for image encryption. Thorough comparison with other image encryption algorithms is also carried out.