In the context of secure medical image processing, patient authentication and data confidentiality are critical challenges. This study presents an enhanced reversible data hiding approach in DICOM images by leveraging halftone-based watermarking, deep learning denoising, and robust segmentation. We introduce a preprocessing stage in which the grayscale medical image is first converted into a halftone image using the Jarvis error-diffusion technique. Then a segmentation algorithm isolates bright regions, ideal for embedding. Binary data is inserted into the white areas using a modified version of the “Data Hiding in Binary Images with High Payload” method. To preserve visual quality, a denoising neural network restores the image with minimal distortion. The embedding process follows the fragile-watermarking scheme, combining chaotic encryption with LSB insertion. This enables embedding of large watermarks and detecting tampering, as any alteration corrupts the embedded data. Additionally, it allows the extraction of the modified region. Experimental results show that the proposed method achieves a PSNR of up to 67.00 dB when comparing the restored grayscale image with the original, indicating excellent visual fidelity and robustness against distortions such as salt-and-pepper noise. This work improves the efficiency and capacity of data embedding in medical images, while ensuring secure patient identification and tamper detection.

In the field of medical image management, the security and privacy of patient data are of utmost importance. This research focuses on the application of the Jarvis halftone technique for image reconstruction aimed at enhancing data hiding capabilities. We employed data hiding in binary image with high payload method to embed binary characters into the modified contours of Jarvis halftone images, thereby increasing the bandwidth for data hiding. To enhance the hiding capacity, the original patient image was modified by adding an artificial contour that creates vectors in selected pixels, thus optimizing the halftone technique used. The processed images were embedded into DICOM medical images using a pseudo-random walk technique, which allowed the generation of two keys for concealment, embedded in layers 12 and 16 of the image. This resulted in a modified DICOM image with a Structural Similarity Index measure. Subsequently, data were extracted using the generated keys, thus retrieving the Jarvis halftone image with modified contours. This process was followed by a conversion of the halftone image to grayscale for patient authentication, highlighting the effectiveness of the Jarvis halftone in preserving image quality after the data hiding and extraction process. This study not only demonstrates the feasibility of integrating advanced halftone and data hiding techniques in the medical field but also opens ways to future research on security in the transmission and storage of medical images, ensuring the confidentiality and integrity of patient information.

In the last decade, the development and accessibility of image editing software have significantly increased, with many open-access tools available online. In this context, image-splicing attacks have gained popularity for their ability to manipulate images effectively. Consequently, there is a need to develop technologies capable of detecting splicing attacks using artificial intelligence systems. This paper introduces a method based on neural networks that identifies unique image features for detecting and localizing splicing manipulations by comparing the extracted features. The proposed approach used a neural network model with two identical branches to extract and compare features from the original and tampered images. Subsequently, the detected manipulated regions are divided into smaller blocks, and eigenvalues associated with each region are computed and compared with those of the original image. This process enables more efficient splicing detection. Experimental results demonstrate the method’s effectiveness against various splicing attacks, achieving a precision and efficacy of 90%. This study offers an innovative system for detecting and locating splicing image forgeries supported by comprehensive experimental validation.

Abstract Invertible colour‐to‐grey algorithms protect the colour of an image against illegal usage and distribution. These schemes hide the colour information into a grayscale version of the image, making colour recovery without proper authorization challenging. A previous study proposed the incorporation of content security by distorting the protected image to hinder illegal structure restoration. This work addresses the limitations of this method, significantly improving both processing time and the quality of the recovered colour image. Specifically, processing times are reduced from 45 h to 9 s, and the mean PSNR value is increased by up to 2.23 dB. Furthermore, this proposal achieves competitive results compared to existing approaches that do not implement content security, obtaining mean peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) values above 42.32 dB and 0.992, respectively. Finally, quantitative results are supported by visual comparisons.

In recent years, the reversible data hiding techniques also known as lossless or invertible data hiding, has gradually become a very active research area. The reversibility of these schemes makes possible to extract the embedded data without errors, as well as to restore the cover medium to its original state. Furthermore, to guarantee the security and confidentiality of the hidden data and the image, reversible data hiding schemes over encrypted domain are presented as a promising solution to solve several issues of information security. This paper presents a study case of reversible data hiding schemes over encrypted domain oriented to the protection of the sharing and distribution of color images hosted in cloud storage services. The experimental results are presented in terms of imperceptibility, capacity, confidentiality, and visual quality, respectively.

In this paper, we propose software protection algorithm against illegal copy by unauthorized persons using software watermarking. In the proposed scheme, the software license information, related to the media access control (MAC) address and software product-key, is embedded into the software logotype image as a watermark sequence. At the first stage of the software execution, the watermark sequence is extracted from the logotype and compared with the current MAC address of the hardware devise. If the extracted MAC address differs from the hardware's MAC address, then the software displays a warning message related to the invalid license and finalizes its execution, otherwise the software continues its normal operation. The performance of the proposed scheme is analyzed from the difficulty of software piracy and reverse engineering points of view, and the evaluation results show the effectiveness of the proposed algorithm.

Video transcoding is a legitimate operation widely used to modify video format in order to access the video content in the end-user's devices, which may have some limitations in the spatial and temporal resolutions, bit-rate and video coding standards. In many previous watermarking algorithms the embedded watermark is not able to survive video transcoding, because this operation is a combination of some aggressive attacks, especially when lower bit-rate coding is required in the target device. As a consequence of the transcoding operation, the embedded watermark may be lost. This paper proposes a robust video watermarking scheme against video transcoding performed on base-band domain. In order to obtain the watermark robustness against video transcoding, four criteria based on Human Visual System (HVS) are employed to embed a sufficiently robust watermark while preserving its imperceptibility. The quantization index modulation (QIM) algorithm is used to embed and detect the watermark in 2D-Discrete Cosine Transform (2D-DCT) domain. The watermark imperceptibility is evaluated by conventional peak signal to noise ratio (PSNR) and structural similarity index (SSIM), obtaining sufficiently good visual quality. Computer simulation results show the watermark robustness against video transcoding as well as common signal processing operations and intentional attacks for video sequences.

In this paper, a robust watermarking method against geometric distortions and common signal processing is proposed. This method is based on two modifications in the 2D histograms. In the first modification, a selected region of 2D histogram composed by R-G color components is modified according to the watermark pattern. In the second modification, another 2D histogram composed by B component and image feature is dynamically partitioned to embed the watermark pattern. The experimental results show robustness against several geometric distortions, common signal processing attacks and some combination attacks. Also the comparison with the prior methods shows the better performance of the proposed method.