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Today’s technological era, the booming desire for e-healthcare has inflated the attention towards the security of data from cyber attacks. As the digital medical images are transferred over the public network, there is a demand to shield an adequate level of protection. One of the prominent techniques is encryption which secures the medical images. This paper recommends a DICOM image encryption based upon chaotic attractors on frequency domain by integer wavelet transform (IWT) and fused with deoxyribonucleic acid (DNA) sequence on the spatial domain. The proposed algorithm uses a chaotic 3D Lorenz attractor and logistic map to generate pseudo-random keys for encryption. The algorithm involves subsequent stages, i.e. permutation, substitution, encoding, complementary and decoding. To endorse the resistance of the proposed algorithm, various analyses have been examined for 256 × 256 DICOM images by achieving an average entropy of 7.99, larger keyspace of 10238 and non-zero correlation. The overall results confirm that the proposed algorithm is robust against the brute force attacks.

In this growing era, a massive amount of digital electronic health records (EHRs) are transferred through the open network. EHRs are at risk of a myriad of security threats, to overcome such threats, encryption is a reliable technique to secure data. This paper addresses an encryption algorithm based on integer wavelet transform (IWT) blended with deoxyribo nucleic acid (DNA) and chaos to secure the digital medical images. The proposed work comprises of two phases, i.e. a two-stage shuffling phase and diffusion phase. The first stage of shuffling starts with initial block confusion followed by row and column shuffling of pixels as the second stage. The pixels of the shuffled image are circularly shifted bitwise at the first stage of diffusion to enhance the security of the system against differential attack. The second stage of diffusion operation is based on DNA coding and DNA XOR operations. The experimental analyses have been carried out with 100 DICOM test images of 16-bit depth to evaluate the strength of the algorithm against statistical and differential attacks. By the results, the maximum entropy has been obtained an average of 15.79, NPCR of 99.99, UACI of 33.31, and larger keyspace of 10140, which infer that our technique overwhelms various other state-of-the-art techniques.

In today’s tech-driven world, secure communication of medical information is a critical necessity. Protecting the patient’s sensitive medical data through encryption algorithms based on chaos theory has emerged as a prominent research trend. This research proposes a novel 2D-Modified Tinkerbell Map with Henon Map (2D-MTMHM) chaotic equation to generate the pseudo-random key sequences for medical image encryption. Combining the Tinkerbell map with the Henon map exhibits a broader range of chaotic behaviour, making it highly suitable for cryptographic applications. The nature, randomness and sensitivity of the developed 2D-MTMHM equation are validated through the NIST SP800-22 statistical test, bifurcation diagram, Lyapunov exponent, permutation entropy, attractor trajectory, sample entropy and sensitivity test. The generated random key sequences trigger the proposed medical image encryption algorithm, which integrates a shuffling-diffusion process. The shuffling unit of the proposed medical image encryption scheme consists of three distinct phases: row-wise shuffling, column-wise shuffling, and selective shuffling based on cut-off points. The diffusion unit is designed to bit-wise scramble the pixel-shuffled image, further enhancing the randomness and security of the encrypted image. Simulation and experimental analysis demonstrate that the encryption system effectively resists statistical, differential and Brute-force attacks. The algorithm achieves an average entropy of 7.99, a correlation coefficient nearer to zero, a Number of Pixels Change Ratio (NPCR) of 99.6%, and a Unified Average Changing Intensity (UACI) of 33.4%. A larger key space of 10 270 is obtained, implying that the algorithm provides security against brute−force attacks.

With the continuous advancement of technology, there is an increasing need for innovative solutions that can handle complex applications such as haptic communications, Internet of Things for smart cities, automation, and manufacturing. One technology that has received much attention is the phase reconfigurable metasurface for reconfigurable intelligent surfaces (RISs). The RIS demands low-power consumption, simple configuration, angular stability, and polarization insensitivity. The use of phase reconfigurable metasurfaces provides benefits such as low cost, low power consumption, and improved communication coverage and quality. This article introduces a reconfigurable combined-loop metasurface that can effectively manipulate phase reflection. This is achieved by incorporating four PIN diodes between two meta-atoms of a 2 × 2 periodic array within a single-layer metallic structure. By controlling the state of the PIN diodes, which can be switched into 16 different states, the metasurface can achieve various phase reflections. The proposed structure has validated a 32× 32 metasurface through numerical simulations and experiments that exhibit promising results, demonstrating its potential for use in 6G applications.

The recent advancement in multimedia technology has undoubtedly made the transmission of objects of information efficiently. Interestingly, images are the prominent and frequent representations communicated across the defence, social, private and aerospace networks. Image ciphering or image encryption is adopted as a secure medium of the confidential image. The utility of soft computing for encryption looks to offer an uncompromising impact in enhancing the metrics. Aligning with neural networks, a Hopfield attractor-based encryption scheme has proposed in this work. The parameter sensitivity, random similarity and learning ability have been instrumental in choosing this attractor for performing confusion and diffusion. The uniqueness of this scheme is the achievement of average entropy of 7.997, average correlation of 0.0047, average NPCR of 99.62 and UACI of 33.43 without using any other chaotic maps, thus proposing attack-resistant image encryption against attackable chaotic maps.

Anomaly detection in surveillance footage is crucial for ensuring protection and safety standards, as it enables the timely identification of unusual or suspicious activities. Recent literature has shown the emergence of graph and hypergraph (HG)-based matching algorithms for object tracking in video frames, facilitating anomaly detection. While these techniques incorporate sampling methods to enhance pace, the task of balancing accuracy and efficiency remains unresolved, particularly when detecting anomalies during simultaneous object tracking. This paper addresses this gap by proposing a unified framework that integrates Game-Theoretic Hypergraph Matching (GTHG) with a Convolutional Autoencoder (CAE). Unlike existing methods that treat tracking and detection separately, the proposed approach combines structural consistency and appearance reconstruction to improve both detection accuracy and computational performance. The proposed method has been tested with a chain of benchmarked films and video clips, and a detailed account of matching between successive frames has been provided. Evaluation metrics, including the Regularity Score, Receiver Operating Characteristics (ROC), and Area Under the Curve (AUC), assess the accuracy of anomaly detection across multiple datasets. Our method achieves an area under the curve (AUC) of 88.7%, 91.2%, and 86.6% on the UCSD Ped1, UCSD Ped2, and CUHK Avenue Datasets, respectively, surpassing the performance of many existing models.

Sensitive multimedia information of all forms is encrypted, with key, before storage and transmission, to protect from illegal use and data manipulation. Since digital images are larger, it’s crucial to encrypt the content, specifically in medical images effectively. In medical image diagnosis, even a small manipulation of data may lead to misinterpretation. This paper addresses this concern by devising an algorithm suitable for encrypting DICOM and other types of images. RC6 cipher is used for encrypting the approximation coefficients (LL), obtained by applying the Haar wavelet transform on the plain image and combined with the redistributed (confused) detailed coefficients (LH, HL, HH). Generation of keys through governing equations of Combined Logistical Tent map, adds to the robustness of the algorithm against attacks. This algorithm works well for all types of images, including DICOM. Among several image databases available, 30 different modalities of images have been taken for experimentation, and promising results have been achieved. Results show that on an average, for an image of bit-depth eight, the proposed encryption algorithm provides the PSNR of 9.0955 dB, the entropy of 7.9990 bits for an encrypted image and, with a UACI of 33.4549 and NPCR of 99.6129, the algorithm could effectively defy the statistical and differential attacks.

Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. In this work, robust respiratory disease classification using breathing sounds ( RRDCBS) is implemented by extracting multiple features from sounds, creating multiple modelling techniques, and experimental identification of diseases using appropriate testing procedures for multi-class and binary classification of respiratory diseases. Decision level fusion of features for Vector quantisation (VQ) modelling technique has provided 100% accuracy for classifying five respiratory diseases and healthy subjects. Decision level fusion of indices on the features has provided 100% accuracy for VQ, support vector machine (SVM), and K-nearest neighbour (KNN) modelling techniques to perform binary classification of the respiratory disease against healthy data sound. Deep recurrent and convolutional neural networks are also evaluated for multiple/binary classification of respiratory diseases.

This paper presents a new hybrid method for concealing images within a cover image using Integer Wavelet Transform and Singular Value Decomposition to enhance the imperceptibility and robustness of hidden images within a cover image. A custom graphical user interface has been developed to aid in evaluating performance metrics, allowing users to assess how various noise conditions affect stego images interactively. The technique shows minimal distortion with a Mean Squared Error of 9.6698 and a Bit Error Rate of 0.7205. Both the Structural Similarity Index Measure and Normalised Cross-Correlation are close to 1 while achieving a Peak Signal-to-Noise Ratio of 29.5665 dB. Our results surpass current methods, particularly when gauging resilience under noise such as Gaussian, speckle, and salt and pepper, proving that this proposed technique effectively maintains high image quality and steganographic security. The results establish the method’s potential for secure image transmission in applications such as defense communications, medical image confidentiality, forensic authentication, and digital rights protection, while providing a reproducible platform for future steganographic research.

Security in e-healthcare applications such as Telemedicine is crucial in safeguarding patients’ sensitive data during transmission. The proposed system measures the patient’s health parameters, such as body temperature and pulse rate, using LM35 and pulse sensors, respectively. The sensor data and the patient’s medical image are encrypted in the Raspberry Pi 3 B + processor using Python’s proposed text and medical image encryption scheme. The encrypted data is transmitted via the Thing Speak cloud and received by another Raspberry Pi at the receiver to decrypt the cipher data. The flask webserver can view the decrypted data by the doctor at the other end. This IoT implementation of secure Electronic Health Record (EHR) transmission employs text and medical image encryption schemes using a Combined Chaotic System (CCS). The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. Then, selective shuffling between the cut-off points breaks the statistical relationship between the neighbouring pixels. Finally, the intra and inter-pixel diffusion is carried out using bit permutation and bit-wise XOR operation to create a highly random cipher image. The initial seed for inter-pixel diffusion is obtained from the hash of intra-pixel diffused images to resist chosen plain text and cipher text attacks. The efficiency of the developed medical image encryption algorithm is tested against various attack analyses. The results and the security analyses validate the effectiveness of the proposed scheme.