Digital Watermarking Research Articles

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods.

A hybrid domain-based watermarking for vector maps utilizing a complementary advantage of discrete fourier transform and singular value decomposition

Digital watermarking plays a crucial role in the copyright protecting of vector maps. Due to its solid theoretical foundation, Discrete Fourier transform (DFT) is frequently used in the construction of watermarking scheme for diverse electronic data. However, when applied to vector maps, DFT is particularly vulnerable to local changes in coordinate points, posing challenges in surviving coordinate point attacks and limiting its practicality. Furthermore, its resistance against geometric attacks is quite weak. To address these issues, we propose an algorithmic complementary strategy that enhances the performance of DFT-based vector map watermarking scheme. The proposed scheme is founded upon a hybrid transform domain consisting of DFT and singular value decomposition (SVD). The first step is to extract feature points using the Douglas Peucker algorithm, with the distance threshold set to a relative value to ensure synchronization of feature points throughout scaling. The feature points are then subjected to DFT to obtain magnitude coefficients, which are then further transformed using SVD. By combining the geometric invariance of magnitude coefficients with the singular vectors of SVD, an invariant with rotation, scaling, and translation invariance can be generated. For watermark embedding, the invariant serves as an embedding domain and are multiplied to minimize the impact on the host vector maps. The experimental results reveal that the proposed vector map watermarking has very low disturbance, substantially lower than the comparing algorithms, deriving in excellent invisibility. The scheme can extract watermark images with a normalized correlation value of 1 under a variety of attacks, including rotation, scaling, translation, vertex addition, simplification, and map cropping, and it can even extract identifiable copyright information under a 10% vertex deletion, demonstrating very comprehensive robustness.

SE-NDEND: A novel symmetric watermarking framework with neural network-based chaotic encryption for Internet of Medical Things

The development of the Internet of Medical Things heavily relies on big data, and data security based on medical images has become a growing concern in society. Digital watermarking serves as a crucial technique for protecting and tracing medical image data copyright, as well as enabling forensic analysis. However, existing deep watermarking methods often neglect the protection of watermarks after extraction, leading to potential copyright disputes. To address this issue, this paper proposes SE-NDEND, a novel symmetric watermarking framework with neural network-based chaotic encryption for the Internet of Medical Things that significantly enhances the effectiveness and security of watermarking while maintaining robustness. Specifically, the SE-NDEND leverages neural networks to simulate chaotic systems and generate chaotic sequences, mitigating the complexity and high cost of implementing chaotic systems using hardware circuits. Moreover, we introduce a new noise layer with Moiré distortion that interacts with the decoder, forming a symmetric network structure that bolsters the robustness of watermarking. Parameters are jointly trained and shared during the training process to counteract potential interference from the noise layer. Experimental results validate the effectiveness of SE-NDEND in enhancing copyright protection, traceability, and forensic capabilities, surpassing existing deep learning methods in terms of visual quality (with PSNR of 45.8492 dB and SSIM of 0.9874), security, and robustness. The proposed framework can find application in protecting medical image data in the Internet of Medical Things.

Analysis of DWT-DCT watermarking algorithm on digital medical imaging.

Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76dB. The proposed model works well for clinical medical images when compared with the existing techniques.

FPGA implementation of robust and low complexity template-based watermarking for digital images

FPGA implementation of robust and low complexity template-based watermarking for digital images

Digital media system design and visual art analysis based on information security

BackgroundWith the growing maturity of Internet technology and 5G transmission technology, digital media systems begin to present more complex scenario applications. Software design security issues are also increasingly evident in digital media systems. This paper analyzes the security issues of digital media system design from the perspective of software protection security. Discussed based on the information security digital media system design and visual art analysis. MethodsThis paper discusses based on the software security protection design and visual art analysis, first proposed the media convergence and information security risk assessment research, including media convergence cognition, information security risk management theory, system information security risk assessment methods and based on the media under the digital certificate. Secondly, the information security algorithm based on digital media technology is studied. From the basic knowledge of digital image, a method of image encryption based on the change of pixel value, the common algorithm of digital image watermarking technology and the encryption technology of digital media are summarized. ResultsExperiments were carried out on three art painting data sets, including Painting91, Oil Painting and Pandora. Compared with the existing label distribution learning methods on the Painting91style dataset, the algorithms SA-IIS and SA-BFGS achieve the classification accuracy of 65.51 % and 66.52 %, respectively. The classification result of the baseline method of VGGNet is 63.89 % on the OilPainting dataset. On the Pandora-style dataset, the classification accuracy of MSCNN1 method is 71.32 %, and the experimental results of CNNF4 method reach 71.76 %. ConclusionThe method proposed in this paper has good application results in different data sets, and the digital media art design system has high reliability and fault tolerance. From the perspective of information security, different index systems have higher security levels.

High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD)

. In this research, we propose an innovative approach that integrates Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) to enhance the quality and security of digital images. The purpose of this technique is to embed imperceptible watermarks into images, preserving their integrity and authenticity. The integration of DCT allows for an efficient transformation of image data into frequency components, forming the basis for embedding watermarks that are nearly invisible to the human eye. In this context, SVD offers an advantage by separating singular values and corresponding vectors, facilitating a more sophisticated watermarking process. The quality evaluation using metrics such as MSE, PSNR, UQI, and MSSIM demonstrates the effectiveness of this approach. Low average MSE values, ranging from 0.0058 to 0.0064, indicate minimal distortion in the watermarked images. Additionally, high PSNR values, ranging from 67.20 dB to 67.22 dB, affirm the high image quality achieved after watermarking. These results validate that the integration of DCT and SVD provides a high level of security while maintaining optimal visual quality in digital images. This approach is highly relevant and effective in addressing the challenges of image protection in this digital era.

Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption

To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope.

AdvEWM: Generating image adversarial examples by embedding digital watermarks

With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific ways (also referred to as adversarial examples) aiming to mislead the DNN models to output incorrect classification results. Similarly, many efforts are proposed to detect and mitigate adversarial examples, usually for certain dedicated attacks. In this paper, we propose a novel framework called AdvEWM based on embedding digital watermark to generate image adversarial examples for fooling DNN models. Specifically, partial main features of the watermark image are embedded into the host image almost invisibly, aiming to tamper with and damage the recognition capabilities of the DNN models. We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples. The experimental results illustrate that the attack success rate on common DNN models can reach an average of 95.47% on the CIFAR-10 dataset and the highest at 98.71%. Besides, our scheme is able to generate a large number of adversarial examples efficiently, concretely, an average of 1.17 s for completing the attacks on each image on the CIFAR-10 dataset. In addition, we design a baseline experiment using the watermark images generated by Gaussian noise as the watermark image dataset that also displays the effectiveness of our scheme. Similarly, we also propose the modified discrete cosine transform (DCT) based Patchwork watermarking algorithm. To ensure repeatability and reproducibility, the source code is available on GitHub.

Retracted: Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

Retracted: Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.

Robust multifunctional watermarking system based zero-watermarking and polar harmonic Fourier moments

The pseudo-randomness exhibited by chaotic systems has proven valuable not only in plaintext information encryption of images with varying dimensions but also as a key signal for protecting image copyright information. Image copyright protection is a crucial part of image information protection. Currently, digital watermarking technology is predominantly employed for copyright protection of digital images. However, most of the existing watermarking technologies primarily focus on the improvement of watermarking performance, neglecting the security of the watermarking system itself. Drawing inspiration from the Latin square rule, this paper proposes a novel hyperchaotic mapping modulation framework, which is employed to construct a new 3D hyperchaotic mapping and enhance the security of the fragile watermark and the zero-watermark generation process. Additionally, it is integrated into the classical image encryption framework of scrambling-diffusion to safeguard meaningful copyright information. Moreover, a functional watermarking system is designed based on the RSA asymmetric encryption algorithm, least significant bit (LSB), and polar harmonic Fourier moments (PHFMs). The proposed system enables identity verification, tampering location, and copyright authentication simultaneously. During the generation of zero watermarks, the binarization threshold of the PHFMs information matrix is determined by the method of maximum variance. The experimental results demonstrate that this scheme achieves a high level of security, better robustness to both conventional and geometric attacks, and precise localization for different types of tampering attacks.

Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography

The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding information into a noise-tolerant signal, such as audio, video, or images, digital watermarking and steganography techniques can be used to protect sensitive intellectual property (IP) and enable confidential communication, ensuring that the information embedded is only accessible to authorized parties. This survey provides an overview of recent developments in deep learning techniques deployed for data hiding, categorized systematically according to model architectures and noise injection methods. In addition, potential future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated. This contribution furthers the creation of a more trustworthy digital world and advances responsible artificial intelligence (AI).

An improved video watermarking algorithm with extraction using a mobile device camera

The use of screen-capture-resistant digital watermarks is a promising way to store information invisibly in a video stream for later retrieval by the user using a smartphone camera. However, the development of algorithms that implement this scenario is associated with the problem of balancing between the imperceptibility of embedding and robustness. A serious problem is the extraction of watermarks using a mobile device. Most people use the vertical positioning of the smartphone when shooting, which excludes the possibility of only marked video sequences entering the frame. The extraction algorithm first finds the screen area in the image and then extracts the watermark under various distortion conditions. This study proposes an approach to improve the efficiency of the algorithm for embedding digital watermarks into video data based on rectangular patterns, which provides resistance to screen shooting. The proposed approach to increasing the embedding imperceptibility provided an increase in the PSNR and SSIM values by 17.18 % and 7.90 %, respectively. The use of a neural network at the extraction stage reduced the BER value by 64.64 %.

A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme

In the digital age, protecting the ownership and data veracity of digital documents is a major challenge. To address the issues concerning copyright protection and data verification of digital media, digital watermarking has emerged as a solution. In this paper, we aspire to make a modest contribution to this emerging and exciting field by presenting our proposed adaptive hybrid image watermarking approach that combines Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Our method involves applying DWT to both the host image and watermark, followed by singular decomposition using SVD on the Low-Low (LL) component of both images. Now modify the singular values of the host image by the singular values of the watermark, and then inverse SVD is applied, followed by inverse DWT, to obtain the watermarked image. After that, the reverse process is applied to obtain the watermark image. Finally, we evaluate our approach’s performance by measuring the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked image as well as the Normalized Cross-Correlation (NCC) between the original and extracted watermark. Simulation results indicate that the proposed method is rich in terms of robustness, imperceptibility and capacity than the previously presented schemes.

A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %.

A comparative study of steganography using watermarking and modifications pixels versus least significant bit

This article presents a steganography proposal based on embedding data expressed in base 10 by directly replacing the pixel values from images red, green blue (RGB) with a novel compression technique based on watermarks. The method considers a manipulation of the object to be embedded through a data compression triple process via LZ77 and base 64, watermark from low-quality images, embedded via discrete wavelet transformation-singular value decomposition (DWT-SVD), message embedded by watermark is recovered with data loss calculated, the watermark image and lost data is compressed again using LZ77 and base 64 to generate the final message. The final message is embedded in portable network graphic (PNG) images taken from the Microsoft common objects in context (COCO), ImageNet and uncompressed color image database (UCID) datasets, through a filtering process pixel of the images, where the selected pixels expressed in base 10, and the final message data is embedded by replacing units’ position of each pixel. In experimentation results an average of 40 dB in peak signal noise to ratio (PSNR) and 0.98 in the similarity structural index metric (SSIM) evaluation were obtained, and evasion steganalysis rates of up to 93% for stego-images, the data embedded average is 3.2 bpp.

Copyright protection of deep image classification models

With the growing number of tasks solved using deep learning methods, the need for protection against unauthorized distribution of the intellectual property such as pre-trained models of deep neural networks is growing. To date, one of the most common ways to protect copyright in the digital space is through embedding digital watermarks. When solving the problem of watermark embedding, an important criterion is the preservation of the model prediction accuracy after introducing the protective information. In this paper, we propose a method for embedding digital watermarks into image classification models based on adding images obtained by superimposing pseudo-holograms on images of the original dataset to the training set. A pseudo-hologram is an image synthesized on the basis of a given binary sequence by arranging pulses for bit encoding in the spectral region. Results of the experimental study show that the proposed method allows one to maintain the classification quality, while also retaining its performance regardless of the architecture of the protected neural network. The conducted series of attacks on protected models show that attempts of an attacker to completely remove the watermark will almost inevitably lead to a significant loss in the model prediction quality. The results of the experiments also include recommendations on the choice of method parameters, such as the size of the trigger and training sets, as well as the length of sequences encoded by pseudo-holograms.

Deepfake Forensics: Identifying Real Regions in Altered Videos with Digital Watermarking

This paper describes a method for detecting Deepfake videos using a lightweight yet secure video encryption algorithm. With the increasing use of digital media, transferring data via the Internet or other mediums requires protection. In the proposed method a digital signature is generated and encrypted using Asymmetric Encryption (RSA). This encrypted signature is then used as a blind watermark for the video. This technique aims to detect “face swap” type of Deepfake videos. It is an efficient algorithm and has minimal impact on the perceptibility of the video quality.

Watermarking using DCT and DWT on Pneumonia images

Watermarking is a branch of the data hiding technique. Watermarking is a technique used to insert a copyright label on an image, so that the copyright of the image can be protected. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are techniques that can be used to watermark. In this study, the Discrete Cosine Transform and Discrete Wavelet Transform methods will be used to watermark images to 5 different host images. In the tests carried out, watermarking techniques will be compared using DCT, DWT, DCT-DWT combination and DWT-DCT combination. The results obtained in this study were the highest PSNR value obtained at 41.931, the highest SSIM obtained 0.99515, the highest entropy was also obtained at 7.4186, The best UACI value is 0.0071158 and the best NCPR value is obtained at 93.9068% then, for the best CC value is obtained at 0.99953. As well as the NCC value, the value obtained is the same all in each test, namely with a value of 1.