Digital Watermarking Research Articles

An Optimized and Secured Image Watermarking and Its Dual Authentication for Internet of Medical Things

An Optimized and Secured Image Watermarking and Its Dual Authentication for Internet of Medical Things

Optimization technique for optimal location selection based on medical image watermarking on healthcare system

The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal.

A dimensionality reduction-based approach for secured color image watermarking

A dimensionality reduction-based approach for secured color image watermarking

UDTCWT-PHFMs Domain Statistical Image Watermarking Using Vector BW-Type R Distribution

For any image watermarking algorithm, how to achieve the trade-off among robustness, imperceptibility, and watermark capacity is a challenging problem because of their mutually constrained relationship. In this paper, we design a statistical-based image watermarking system to solve the trade-off problem. In embedding process, consider the imperceptibility and the robustness, we inventively combine the undecimated dual tree complex Wavelet transform (UDTCWT) and the polar harmonic Fourier moments (PHFMs) to obtain the UDTCWT-PHFMs magnitudes as the watermark carriers, and we embed watermark signals in multiplicative manner. In modeling phase, by analyzing the statistical property and the multiple correlations of the UDTCWT-PHFMs magnitudes, we bound the magnitudes of different directions into the vector groups, and then we model them with the vector Roy type bivariate Weibull (BW-Type R) distribution so that we can capture accurately the marginal characteristics, the inter-scale dependencies, and the inter-orientation dependencies at the same time. Moreover, we obtain the model parameters with the double looped maximum likelihood estimation (double-looped MLE). Benefit from the reliable modeling result, we finally derive a novel specific image watermark decoder to blindly extract the hidden watermarks. Extensive experiment results declare the designed statistical image watermarking system achieves the better balance among imperceptibility, robustness, and payload.

Adaptive image watermarking using bidimensional empirical mode decomposition

Digital watermarking is considered one of the technological means used to guarantee the security and authenticity of data transmitted over communication systems. A new method of image watermarking using the bidimensional empirical mode decomposition (BEMD) will be presented in this article, where the new idea is to use the BEMD of both the cover image and the watermark image. The embedding process consists of adding to each intrinsic modal function (IMF) of the cover image the corresponding IMF of the watermark image. The watermarked image contains three different watermarks and appropriate frequencies, which makes it more robust. We use this method for two reasons: first, to conserve the constituent characteristics of each IMF and second, to ensure the invisibility of the watermark. The results obtained showed a good response against different kinds of attacks. This technique has been compared with other methods of image watermarking based on decomposition BEMD, which showed good results.

A Comparative Study of Various Digital Image Watermarking Techniques: Specific to Hybrid Watermarking

Abstract: Digital security is one of the important aspects of today’s era. Digital content is being grown every day on the internet; therefore, it is essential to guard the copyright of digital content using various techniques. Watermarking has emerged as an important field of study aiming at securing digital content and copyright protection. None of the watermarking techniques can provide well robustness against all the attacks, and algorithms are designed based on required specifications, which means there is a lot of opportunity in this field. Image watermarking is a vast area of research, starting from spatial-based methods to deep learning-based methods, and it has recently gained a lot of popularity due to the involvement of deep learning technology for ensuring the security of digital content. This study aims at exploring important highlights from spatial to deep learning methods of watermarking, which will be helpful for the researchers. In order to accomplish this study, the standard research papers of the last ten years have been obtained from various databases and reviewed to answer the five research questions. Open issues and challenges are identified and listed after reviewing various kinds of literature. Our study reveals that hybrid watermarking performs better in terms of balancing the trade-off between imperceptibility and robustness. Current research trends and future direction is also discussed.

A Copyright-Preserving and Fair Image Trading Scheme Based on Blockchain

With the proliferation of the Internet, particularly the rise of social media, digital images have gradually become an important part of life, and trading platforms have emerged for buying and selling images. However, traditional image trading service providers may disclose users' private information for profit. Additionally, many image trading platforms disregard the fairness of a transaction and the issue of copyright protection after an image is sold. This neglect harms the interests of users and affects their enthusiasm for trading. A secure way to safely transact images is needed. We proposed a copyright-preserving and fair image trading scheme based on blockchain, which combines amplifying locality-sensitive hashing with searchable symmetric encryption to achieve safe image retrieval on blockchain and ensure the credibility of the image retrieval process. Additionally, we use digital fingerprint and watermark technologies to realize the copyright protection of images and use smart contracts to achieve fair transaction processes. The experimental results show that our scheme can protect image copyrights and realize a fair trading process while ensuring efficiency.

Enhancement of DWT-SVD digital image watermarking against noise attack using time–frequency representation

Information security has been defined as one of the most critical issues in the information era, as it is utilized to protect confidential information during transfers in real-world applications. In the case of image encryption, a variety of information security approaches were used. Domain spatial and domain frequency are two domains in which such techniques can be classified. This study uses a combination of the singular value de-composition (SVD) and discrete wavelet transformation (DWT) to construct an encryption method based on a traditional watermarking system. Compared with other traditional methods, the proposed DWT-SVD approach has excellent robustness, and it has been strengthened for having high degree of the robustness against the additive white Gaussian noise (AWGN) attacks by utilizing a de-noising strategy based upon S-transform approach. Compared with DWT algorithm denoising approach, the results reveal that the S-transform denoising algorithm that has been deployed in the present article has a robust protection towards the Gaussian noise attack for mean squared error (MSE) around 0.005 and peak signal to noise ratio (PSNR) around 24 dB.

A Robust and Imperceptible Dark Frame Selection Using Advanced Squeezed Convoluted Ebola Neural Network-Based Video Watermarking Scheme with All Phase Discrete Cosine Biorthogonal Transform

Watermarking has been developed as a beneficial approach to resolve security problems such as copyright defense, legitimate ownership, and authenticity of digital data. Grayscale or binary images and color images are the most important types of embedded watermarks in the existing video watermarking schemes, while dark frame watermarking is barely used. A new robust blind video watermarking system with an Advanced Squeezed Convoluted Ebola Neural Network (ASCENN) approach is suggested to secure copyright, reduce noise, and authenticate the authenticity of dark frames. The proposed method selects the dark frames from the extracted input video. Then the selected dark frames are embedded and extracted using Finite Ridgelet Transform (FRT), All Phase Discrete Cosine Biorthogonal Transform (APDCBT), and Singular Value Decomposition (SVD) techniques. The robustness of the introduced approach is established by evaluating it through various image watermarking techniques and evaluating its performance against geometric, nongeometric, and combinational attacks. While comparing with the other existing video watermarking approaches, the proposed method has obtained a maximum correlation coefficient of 1, and the signal-to-noise ratio of 75[Formula: see text]dB, at minimum error rate of 0.0215, respectively.

Robust Blind Image Watermarking Using Coefficient Differences of Medium Frequency between Inter-Blocks

The existing discrete cosine transform (DCT) differential quantization robust watermarking has poor robustness against JPEG compression, cropping, and combined attacks. To improve such issues, a pair of adjacent block coefficients are selected to reduce the offset and improve the robustness of the watermarking. Firstly, at adjacent positions of neighboring blocks, the differences of medium frequency coefficients are calculated, and then the differences are used to divide regions. Experimental results show that this method is more robust to various attacks than the existing DCT differential quantization robust watermarking. The accuracy of watermark extraction under a JPEG compression attack increased by 2%, while the error rates of watermark extraction under a cropping attack and a combination attack decreased by 4.4% and 9%.

Resampling-Detection-Network-Based Robust Image Watermarking against Scaling and Cutting.

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.

GAN-based watermarking for encrypted images in healthcare scenarios

Nowadays, it is very common for healthcare professionals or staff to transmit digital data in the form of images over public channels or store it on hard drives or third-party clouds. However, unauthorised users and cloud-service providers may view or abuse these sensitive images. This research proposes a generative adversarial network (GAN)-based watermarking for encrypted images to prevent data leakage in healthcare scenarios. The technique uses a combination of a chaotic map and randomised singular value decomposition (RSVD) to encrypt the image first. Subsequently, a GAN model is developed for watermark generation by hiding multiple marks within an image. Later, the encrypted image is marked by embedding the generated watermark for copyright protection and authentication. This fundamentally solves the problem of copyright violation and privacy leakage of medical data. Experimental results have demonstrated that the proposed method is imperceptible and successfully resists various attacks. The obtained results confirmed the superiority of this method over other techniques, which makes it more suitable for healthcare applications.

Creating Digital Watermarks in Bitmap Images Using Lagrange Interpolation and Bezier Curves.

The article is devoted to the introduction of digital watermarks, which formthe basis for copyright protection systems. Methods in this area are aimed at embedding hidden markers that are resistant to various container transformations. This paper proposes a method for embedding a digital watermark into bitmap images using Lagrange interpolation and the Bezier curve formula for five points, called Lagrange interpolation along the Bezier curve 5 (LIBC5). As a means of steganalysis, the RS method was used, which uses a sensitive method of double statistics obtained on the basis of spatial correlations in images. The output value of the RS analysis is the estimated length of the message in the image under study. The stability of the developed LIBC5 method to the detection of message transmission by the RS method has been experimentally determined. The developed method proved to be resistant to RS analysis. A study of the LIBC5 method showed an improvement in quilting resistance compared to that of the INMI image embedding method, which also uses Lagrange interpolation. Thus, the LIBC5 stegosystem can be successfully used to protect confidential data and copyrights.

Chronological bald eagle optimization based deep learning for image watermarking

This work presents a Chronological Bald Eagle Optimization (CBEO)-Deep Learning (DL) approach for performing image watermarking. Here, the watermark is implanted in the cover image, by selecting the optimal region in the cover image with the help of the LeNet. Further, the Haar Wavelet Transform (HWT) is utilized in the embedding procedure to improve the robustness of the approach. The trainable parameters of the LeNet used for selecting the optimal region in the cover image are optimized utilizing the CBEO algorithm. Furthermore, the effectiveness of the HWT + CBEO_LeNet is inspected by considering parameters, such as Normalized Correlation (NC)and Peak Signal-to-Noise Ratio (PSNR), and investigations reveal that the proposed HWT + CBEO_LeNet offered high robustness against various noises and attacks and computed a maximum PSNR, NC, and SSIM of 24.989 dB 0.761, 0.969 and obtained least BER value of 0.047, respectively.

Hiding Full-Color Images into Audio with Visual Enhancement via Residual Networks

Watermarking is a viable approach for safeguarding the proprietary rights of digital media. This study introduces an innovative fast Fourier transform (FFT)-based phase modulation (PM) scheme that facilitates efficient and effective blind audio watermarking at a remarkable rate of 508.85 numeric values per second while still retaining the original quality. Such a payload capacity makes it possible to embed a full-color image of 64 × 64 pixels within an audio signal of just 24.15 s. To bolster the security of watermark images, we have also implemented the Arnold transform in conjunction with chaotic encryption. Our comprehensive analysis and evaluation confirm that the proposed FFT–PM scheme exhibits exceptional imperceptibility, rendering the hidden watermark virtually undetectable. Additionally, the FFT–PM scheme shows impressive robustness against common signal-processing attacks. To further enhance the visual rendition of the recovered color watermarks, we propose using residual neural networks to perform image denoising and super-resolution reconstruction after retrieving the watermarks. The utilization of the residual networks contributes to noticeable improvements in perceptual quality, resulting in higher levels of zero-normalized cross-correlation in cases where the watermarks are severely damaged.

On a self-recovery digital watermarking scheme robust against spatial and temporal attacks on compressed video

In recent decades, the use of digital media has increased. With it, digital media exposure to unwanted manipulations that, although unintentional, can affect third parties. These manipulations in digital watermarking are known as attacks, consisting of intentional or unintentional editing of multimedia content such as images, videos, or audio streams. For video sequences, attacks can be performed in spatial and temporal domains. The former includes changes to individual frames, while the latter involves altering sub-sequences, such as inserting or deleting frames.This work aims to achieve approximate recovery from these attacks using digital watermarking. Unlike most existing works, this scheme uses the compression domain since most video applications use some compression standard to transmit and store these sequences. By adapting popular image block-based popular recovery schemes for large video sequences and using a novel scrambled watermark mapping, satisfactory recovery and visual quality results are achieved.

Digital Watermarks for Videos Based on a Locality-Sensitive Hashing Algorithm

Digital Watermarks for Videos Based on a Locality-Sensitive Hashing Algorithm

Application of multithreading for protection programs

A method for identifying a program copy by means of digital watermark is presented in this article. Previously known analogues for constructing a watermark used modification of program's control flow graph, and reading was made via complex analysis of the program's trace. It is known that applying a protector or packer can neutralize such a watermark. In this paper, it is proposed to use a multi-threaded application that generates numbers with a normal distribution law. The embedding of the digital watermark is carried out by changing the dispersion of distributions. Such random variable can be found via a signature in the process memory and is used both within the program itself and by a third-party when reading the watermark. An experimental selection of the optimal parameters of the algorithm has been carried out. Overhead expenses associated with the creation of a large number of threads are estimated.

Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data. The encrypted medical image undergoes a 2-level DWT process, allowing the concealment of encrypted patient information while maintaining its invisibility. This proposed scheme surpasses others in experimental evaluations, as evidenced by metrics such as PSNR and NC, solidifying its position as a more secure choice for medical image watermarking. The results validate the scheme’s robustness and imperceptibility.

A Dual Blind Watermarking Method for 3D Models Based on Normal Features.

Digital watermarking technology is an important means to effectively protect three-dimensional (3D) model data. Among them, "blind detection" and "robustness" are key and difficult points in the current research of digital watermarking technology based on 3D models. In order to realize the blind detection of a watermark and improve its robustness against various common attacks at the same time, this paper proposes a dual blind watermarking method based on the normal feature of the centroid of first-ring neighboring points. The local spherical coordinate system is constructed by calculating two different normal vectors, and the first pattern watermark and the second random binary sequence watermark are embedded, respectively. The experimental results show that this method can not only realize the blind detection of dual watermarks, but also have the ability to resist common attacks such as translation, rotation, scaling, cropping, simplification, smoothing, noise, and vertex reordering to a certain extent.