Explore the vast range of titles available.

Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New 5D hyperchaotic system combined with a customised U-Net architecture. Chaotic maps have become an increasingly popular method for encryption because of their remarkable characteristics, including statistical randomness and sensitivity to initial conditions. The significant region is segmented from the medical images using the U-Net network, and its statistics are utilised as initial conditions to generate the new random sequence. Initially, zig-zag scrambling confuses the pixel position of a medical image and applies further permutation with a new 5D hyperchaotic sequence. Two stages of diffusion are used, such as dynamic DNA flip and dynamic DNA XOR, to enhance the encryption algorithm’s security against various attacks. The randomness of the New 5D hyperchaotic system is verified using the NIST SP800-22 statistical test, calculating the Lyapunov exponent and plotting the attractor diagram of the chaotic sequence. The algorithm validates with statistical measures such as PSNR, MSE, NPCR, UACI, entropy, and Chi-square values. Evaluation is performed for test images yields average horizontal, vertical, and diagonal correlation coefficients of –0.0018, –0.0002, and 0.0007, respectively, Shannon entropy of 7.9971, Kolmogorov Entropy value of 2.9469, NPCR of 99.61%, UACI of 33.49%, Chi-square “PASS” at both the 5% (293.2478) and 1% (310.4574) significance levels, key space is 2 500 and an average encryption time of approximately 2.93 s per 256 × 256 image on a standard desktop CPU. The performance comparisons use various encryption methods and demonstrate that the proposed method ensures secure reliability against various challenges.

A new multistage encryption algorithm is proposed by integrating the deep neural network with a new 4D multi-scroll chaotic map to enhance the efficiency and improve the security of image transmission in the open channel. This combined network expands the key space and maintains the secrecy of the key with the multistage encryption algorithm. Initially, the image adaptive key generation process is implemented by the EfficientNet-B3 network to extract the features from the source image, which are then converted into hash values using SHA 256. The hash values are partitioned into four sections, and each section is normalized to give one distinct initial value for the generation of a multi-scroll chaotic sequence. The pseudo-parallel process routes the split source sub-image blocks (128⨯128) of plain text to branch 1 or branch 2, decided by the seed value of the chaotic sequence, increasing the high robustness against the differential and statistical attacks. Each branch contains row and column-wise permutations, bidirectional selective shuffling, and chaotic intra/inter-pixel diffusion in varying orders. The key image diffusion and dynamic DNA diffusion to the intermediate cypher image exhibit a strong avalanche effect. The simulation evaluation on the natural data set images demonstrates the large key space of 2 to the power of 674, high key sensitivity, uniform histogram with entropy value attains the critical values of 7.9, high NPCR value of 99.9%, UACI values with 33.46%, almost zero-pixel correlation and strong robustness to the cropping and noise attacks.

A chaos-based cryptosystem requires an extremely nonlinear chaotic map with many chaotic regions. Quantum computers efficiently provide speed and security in image communication. A new two-dimensional triangle function combined with a discrete chaotic map (2D-TFCDM) is proposed in this research. The generated map is tested on various grounds such as attractor plot, bifurcation diagram, sensitivity test, Lyapunov exponent, 0–1 Test, permutation entropy and National Institute of Standards and Technology (NIST) test suite. The proposed map combined with the Secure Hash Algorithm (SHA) is utilised in image cryptography applications. Furthermore, the encrypted image is communicated by the novel enhanced quantum representation (NEQR) method using the qasm_simulator of IBM quantum computer (Qiskit) to utilise the benefits of the laws of physics to secure data. The numerical analyses are done, and simulation results are compared with recent techniques that depict the effectiveness of the image encryption method in resisting various attacks.

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms.

This work proposes a Dual Image Reversible Data Hiding (DIRDH) scheme using interpolation and trinary encoding of secret data. Trinary encoding shows promise because it encodes 3-bit binary data to 2-bit trinary data from the set {−1, 0, 1}. This can be directly used as shifts in the cover pixel values to embed the data without any further processing. The main issue is that the encoding generates similar codes for some combinations of binary bits and makes it necessary to use auxiliary data. Thus, the auxiliary data becomes very difficult to handle with increasing payloads and needs to be sent as a separate file to the receiver. The proposed scheme avoids this problem by using interpolation to create space for the secret data while completely eliminating the need for auxiliary data. The original image is first interpolated to create the cover image and the trinary encoding is used to generate the shifts. The shifts are added to the interpolated pixels to generate the stego image. The modification to the processing of the cover image helps to eliminate the location map completely. Results show that the stego images have an average Peak Signal to Noise Ratio (PSNR) of 50.42 dB while improving the effective embedding capacity from 1.1 bits per pixel (bpp) to 1.2bpp. This is an improvement of about 9% over the previous trinary encoding schemes.

Reversible data hiding (RDH) plays a key role in ensuring security of communications. Recent studies have revealed that RDH techniques based on interpolation are gaining popularity because of the ease of implementation and support for high embedding capacity. From recent literature, it is noted that the one-dimensional (1D) parabolic interpolation-based data hiding technique is suitable for high embedding capacity applications. This study aims to implement a high capacity RDH technique using a new two-dimensional (2D) parabolic interpolation and a novel embedding technique that is suitable for interpolation-based techniques. The results of this study showed that the proposed 2D parabolic interpolation maximized the utilization of the cover image pixels in up-sampling. The technique produced interpolated images of a higher quality compared to the 1D parabolic interpolation technique. Moreover, the proposed data hiding technique exploited the local redundancy and obtained the high embedding capacity with an appreciable image quality better than other state-of-the-art techniques. The results of this study support the view that the proposed parabolic interpolation has the potential to address security issues in high embedding capacity applications.

The usage of digital health data such as documents, images and videos has increased drastically in recent years, making them more prone to sophisticated cyber threats. Here arises a great requirement of information security since this digital data is sent through the public network. Many encryption algorithms were utilised to protect digital data from typical attacks. There exist several conventional encryption algorithms such as Data Encryption Standard (DES), Advanced Encryption Standard (AES), International DataEncryption Algorithm (IDEA), etc., which are used for encryption purposes. Still, they take longer execution time, provides poor security and are more vulnerable to several cyber-attacks. The proposed work provides a cryptosystem based on 4D Lorenz type hyper-chaos and Deoxyribonucleic acid (DNA) encoding mechanism to overcome earlier method limitations. The approximated and detailed coefficients of the input image is obtained by applying Integer Wavelet Transform (IWT). Then, the pixels of the Low-Low (LL) band get permuted using a Logistic map. The 4D hyper-chaotic system creates a pseudo-random chaotic sequence using the initial values, quantised to a keystream. The final data gets encoded using the DNA encoding rule. For enhanced diffusion, DNA-XOR is performed to produce the final cipher image. Various performance metrics have been analysed for several images, and the experimental results show that the proposed scheme is effective against brute force attacks.

In recent years, technological advancements have made the transmission of confidential information spooky. This research proposes a modified 5D chaotic map and a new image encryption algorithm based on an integrated chaotic system developed with SHA-512 hashing and a confusion-diffusion architecture. The modified 5D chaotic map provides randomness, and its performance is evaluated through a bifurcation diagram and Lyapunov exponent. The randomness of chaotic sequences is validated through the NIST test. The multi-round diffusion and permutation incorporating the proposed chaotic sequences significantly enhances security by destroying pixel correlation among pixels. The encryption algorithm is validated through performance metric analysis, yielding NPCR of 99.6069%, UACI of 33.4284%, and entropy of 7.99442. These values depict advanced security features needed for various multimedia, medical, and military applications. Therefore, this approach reveals the extent to which chaotic encryption systems provide digital image protection in high-risk communication environments.

Cryptography is a method for secure communication by hiding information with secret keys so that only authorised users can read and process it. Efficient random sequence generators provide robust cipher design for cryptographic applications; further, these sequences are used for data encryption. In this paper, the highly chaotic nature of hybrid chaos maps and neural network is combined to build a random number generator for cryptographic applications. A custom neural network with a user-defined layer transfer function is built to increase the generator’s randomness. In this work, the two-hybrid chaotic map’s control parameters and iteration value are designed as a layer transfer function to obtain high randomness. Colour image encryption is performed with the extracted sequences and deoxyribonucleic acid encoding technique. Various tests like NIST, attractor test and correlation are applied to the generator to show the degree of randomness. Simulation analysis such as keyspace, key sensitivity, statistical, differential analysis, and chosen-plaintext attack shows the encryption algorithm’s strength.

One of the fastest-growing industries in recent years has been e-Healthcare. Many cyberattacks and threats against patient confidentiality exist in electronic health records (EHRs). To shield EHRs from data breaches and to secure the data with integrity, DNA subsequences, SHA-256, and Hyper Chaotic Multi Attractors Chen System (HCMACS) are proposed for effective medical image encryption. A combined HCMACS produces a pseudorandom key sequence to strengthen its resiliency. The two significant advantages of the proposed technique are integrity and robustness, where the secret keys are susceptible to initial states determined by the original image’s hash value. Furthermore, the encrypted image is uploaded to a cloud-based service where an authorised user can retrieve the original data. Finally the digital medical images have confidentiality, integrity and availability (CIA). The outcomes of the DNA-based cryptosystem for medical images are validated with several analyses and are efficiently defended against statistical, differential, and chosen-plaintext attacks.