ABSTRACT As the volume of digital data increases, there is an increasing need for effective compression methods to address storage demands. Concurrently, the importance of robust image watermarking for authentication and ownership verification cannot be overstated. This work tackles the dual challenge of optimizing image compression for storage conservation and implementing strong image watermarking for copyright protection. The suggested approach integrates the K‐means clustering compression algorithm to enhance storage efficiency along with a resilient image watermarking technique based on spatial‐domain embedding. We introduce a blind robust watermarking approach that uses zero‐frequency coefficient alteration independently in the spatial domain instead of using the discrete cosine transformation (DCT) to verify the ownership of colored images. To enhance the robustness of the system, we have incorporated two watermarks into the cover image. This precaution ensures that even if one watermark undergoes deterioration due to attacks, authentication can still be assured by recovering the other watermark. Compared to frequency‐domain approaches, our scheme yields better robustness and reduced computing complexity. The average peak signal‐to‐noise ratio (PSNR) for the test images using our approach is above 39 dB with a compression ratio equal to 5.9978, removing up to 83% of the redundancy of the host image. After comparing our approach with several state‐of‐the‐art methods, its robustness is exposed by the values of normalized correlation coefficient (NCC) close to one and bit error rate (BER) values close to zero. Besides, the scheme is able to embed a total of 8192 watermark bits in the host image of size 512 × 512 × 3. Experimental results affirm the effectiveness of the proposed methodology, marking it as a valuable contribution to the domains of image processing and information security.

Cyberattacks pose a serious threat to the wireless transfer of sensitive healthcare data, hampering the level of privacy offered. Numerous cyber-security modules have been developed. However, many of these methods are unsuitable for real-time medical data processing. In this article, we present a cybersecurity framework developed for medical images in a smart healthcare system. We propose two novel two-dimensional chaotic maps, called the logistic regulated quadratic map (LRQ) and quadratic regulated quadratic map (QRQ), which have compound chaotic properties and a large chaotic range. We present an encryption technique based on the LRQ and QRQ map and demonstrate that the exceedingly chaotic pseudorandom number sequence generated by the maps results in a highly robust cipher image. Our fast cryptosystem can encrypt an image of size 128 × 128 in approximately 0.03 s. The proposed cybersecurity solution protects data against cyberattacks and ensures a seamless treatment experience.

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Abstract Underwater imaging is one of the hot areas of research and is receiving considerable attention from the research community due to the challenges involved. Underwater images are prone to various distortions like poor contrast and colour deviation. Light scattering and light absorption in water medium are the main reasons for degradation in subaquatic images. Scattering of visible‐light energy reduces the sharpness of image whereas varying degrees of light attenuation travelling in water results in the colour change. Restoration of distorted underwater images is an ill‐posed and challenging problem. Various techniques and methodologies are being used to process and restore underwater images. In this study, we present a state‐of‐the‐art review of various conventional and computer vision‐based algorithms and techniques, developed so far, to present a clearer view of the methods used for underwater image restoration. We discuss various conditions for which the schemes have been developed as well as highlight the quality assessment methods used to evaluate their performance. We compare various state‐of‐art schemes based on various subjective and objective indices and discuss future research directions in the field of underwater image restoration.

Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content. The proposed approach is based on novel left data mapping (LDM), pixel repetition method (PRM), RC4 encryption, and checksum computation. The input image of size [Formula: see text] is upscaled by using PRM that guarantees reversibility with lesser computational complexity. The binary secret data are encrypted using the RC4 encryption algorithm and then the encrypted data are grouped into 3-bit chunks and converted into decimal equivalents. Before embedding, these decimal digits are encoded by LDM. To embed the shifted data, the cover image is divided into [Formula: see text] blocks and then in each block, two digits are embedded into the counter diagonal pixels. For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels. A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection. The average peak signal to noise ratio (PSNR) of the stego-images obtained is 41.95 dB for a very high embedding capacity of 2.25 bits per pixel. Furthermore, the embedding time is less than 0.2 s. Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper. All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT-based smart health.

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.

The image data received through various sensors are of significant importance in Industry 4.0. Unfortunately, these data are highly vulnerable to various malicious attacks during its transit to the destination. Although the use of pervasive edge computing (PEC) with the Internet of Things (IoT) has solved various issues, such as latency, proximity, and real-time processing, but the security and authentication of data between the nodes is still a significant concern in PEC-based industrial-IoT scenarios. In this article, we present “DWFCAT,” a dual watermarking framework for content authentication and tamper localization for industrial images. The robust and fragile watermarks along with overhead bits related to the cover image for tamper localization are embedded in different planes of the cover image. We have used discrete cosine transform coefficients and exploited their energy compaction property for robust watermark embedding. We make use of a four-point neighborhood to predict the value of a predefined pixel and use it for embedding the fragile watermark bits in the spatial domain. Chaotic and deoxyribonucleic acid encryption is used to encrypt the robust watermark before embedding to enhance its security. The results indicate that DWFCAT can withstand a range of hybrid signal processing and geometric attacks, such as Gaussian noise, salt and pepper, joint photographic experts group (JPEG) compression, rotation, low-pass filtering, resizing, cropping, sharpening, and histogram equalization. The experimental results prove that the DWFCAT is highly efficient compared with the various state-of-the-art approaches for authentication and tamper localization of industrial images.

The proliferation of information and communication technology has made exchange of information easier than ever. Security, Duplication and manipulation of information in such a scenario has become a major challenge to the research community round the globe. Digital watermarking has been found to be a potent tool to deal with such issues. A secure and robust image watermarking scheme based on DC coefficient modification in pixel domain and chaotic encryption has been presented in this paper. The cover image has been divided into 8×8 sub-blocks and instead of computing DC coefficient using Discrete Cosine Transform (DCTI, the authors compute DC coefficient of each block in spatial domain. Watermark bits are embedded by modifying DC coefficients of various blocks in spatial domain. The quantum of change to be brought in various pixels of a block for embedding watermark bit depends upon DC coefficient of respective blocks, nature of watermark bit (0 or 1) to be embedded and the adjustment factor. The security of embedded watermark has been taken care of by using chaotic encryption. Experimental investigations show that besides being highly secure the proposed technique is robust to both signal processing and geometric attacks. Further, the proposed scheme is computationally efficient as DC coefficient which holds the watermark information has been computed in pixel domain instead of using DCT on an image block.