Watermarking Technique Research Articles

A Comprehensive Approach to Image Protection in Digital Environments

Protecting the integrity of images has become a growing concern due to the ease of manipulation and unauthorized dissemination of visual content. This article presents a comprehensive approach to safeguarding images’ authenticity and reliability through watermarking techniques. The main goal is to develop effective strategies that preserve the visual quality of images and are resistant to various attacks. The work focuses on developing a watermarking algorithm in Python, implemented with embedding in the spatial domain, transformation in the frequency domain, and pixel modification techniques. A thorough evaluation of efficiency, accuracy, and robustness is performed using numerical metrics and visual assessment to validate the embedded watermarks. The results demonstrate the algorithm’s effectiveness in protecting the integrity of the images, although some attacks may cause visible degradation. Likewise, a comparison with related works is made to highlight the relevance and effectiveness of the proposed techniques. It is concluded that watermarks are presented as an additional layer of protection in applications where the authenticity and integrity of the image are essential. In addition, the importance of future research that addresses perspectives for improvement and new applications to strengthen the protection of the goodness of pictures and other digital media is highlighted.

Visually Encrypted Watermarking for Ordered-Dithered Clustered-Dot Halftones

Visual encryption and show through watermarking are widely used techniques to hide secret data in halftone images. The secret watermark can be quickly revealed when the halftone images are printed in transparency and overlaid on each other. The present studies emphasize developing a show-through watermarking technique for the clustered-dot halftone types. The proposed method exploits the properties of various configurations of dither array screens constructed using distinct Gaussian filters to embed watermarks. Two approaches are proposed, i.e., adjacent dither array pairs and the dither array translations. The optimal configuration of dither array parameters is developed to obtain maximum contrast and imperceptibility. Moreover, a new edge screen dithering is proposed to eliminate the edge artifacts, resulting in smooth screen transitions. As efficient ordered dithering is adopted for the watermark embedding, the joint watermarking and halftoning can be performed without additional computations. In comparison to the existing state-of-the-art show-through watermarking techniques, the proposed method can present superior image quality, computational simplicity, and decoded watermarks can be perceived with clear contrast when the images are overlaid with each other.

Watermarking and Encryption Technique to Avoid Data Leakage in Cloud Storage with Block Chain Technology

Multimedia data sharing is increasingly becoming an essential part of the daily life for end users to access different systems, services, and applications. Data disclosure frequently occurs in real-world cloud storage services. Authentication and copyright protection of multimedia contents has always been a concern in secure data transfer media. The problem has become more critical with the increasing use of the Internet and digital technologies. However, making the protection of copyright is more complex and difficult. In proposed approach both Watermarking and Proxy Re-encryption (PRE) approach utilized for efficient multimedia content sharing. Watermarking is used to hiding the information such as hide secret information in digital media like images. Encryption techniques used to provide security to data. In proposed work, secret key can be encrypted using encryption algorithm with the help of key. Then user’s private key combined with encrypted key information and can be embedded within the image or audio using LSB (Least Significant Bit). After embedding of secret information, image or audio can be encrypting using ECC Encryption algorithm. Finally, authorized user can extract decryption key with the help of embedded data verification process. Unauthorized or illegal access can identify, when user information does not match with embedded information. And also secure these transaction details using Block chain technology.

Compression Run Length Encoding On Watermarking Using a Combination of DCT, DWT and SVD

This study focus on identifying medical images by proposing the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) transformation watermarking technique and prove an increase the quality of watermarked images is good in terms of imperceptibility. Due to reduce the need for data memory compression is applied to the host image, where the compression technique chosen is lossless so that the compressed host image experiences a decrease in file size while maintaining data integrity, to maintain image degradation perceptions and diagnostic quality standards during the watermarking process. Here, we use DWT-DCT-SVD and Run Length Encoding (RLE). A good Peak Signal to Noise Ratio (PSNR) more than 30 dB using over than 200 compression size. The extracted watermark image is quite good with a fairly high PSNR value. The highest compression result size is in 32.2511.

An Overview of Robust Video Watermarking Techniques

Copyright hacking and piracy have increased as the Internet has grown in popularity and access to multimedia material has increased. Security, property protection, and authentication have all been achieved via watermarking techniques. This paper presents a summary of some recent efforts on video watermarking techniques, with an emphasis on studies from 2018 to 2022, as well as the various approaches, achievements, and attacks utilized as testing measures against these watermarking systems. According to the findings of this study, frequency-domain watermarking techniques are more popular and reliable than spatial domain watermarking approaches. Hybrid DCT and DWT are the two most used techniques and achieve good results in the field of video watermarking, and the system is more robust against attacks. PSNR was taken into consideration as a comparison metric in various recent articles in terms of robustness in the video watermarking field.

A new efficient anaglyph 3D image and video watermarking technique minimizing generation deficiencies

A new efficient anaglyph 3D image and video watermarking technique minimizing generation deficiencies

A secure fractional-order polar harmonic transforms based image watermarking scheme using funk singular value decomposition

In addition to existing cryptographic systems, watermarking technologies have been developed to add extra security. Digital watermarking utilizes embedding or hiding techniques to protect multimedia files from copyright violations. Fundamental procedures of digital watermarking techniques are embedding and extraction. Singular value decomposition (SVD) based Image watermarking schemes become popular owing to its better trade-off among robustness and imperceptibility. Nevertheless, false positive problem (FPP) is a major issue of SVD-based watermarking schemes. The singular value that is a fixed value and does not contain structural information about image is the primary cause of FPP problem. Therefore, Message Digest algorithm image watermarking scheme based on Funk Singular Value Decomposition and Fractional-Order Polar Harmonic Transform (FSVD-FOPHT) is proposed in this paper to address this problem. The MD-5 algorithm is used to extract data from the host and watermark imageries and then create secret key. The FSVD-FOPHT method is utilized to hide watermark information in host image. The secret keys are extracted from hided image using inverse process of Fractional-Order Polar Harmonic Transforms with Funk Singular Value Decomposition algorithm. By using the extraction procedure, watermark image is extracted, and then reconstructs original watermarked image. During extraction procedure, the secret key is used for authentication to address FPP. Then, the proposed method is implemented in MATLAB and performance is analyzed with evaluation metrics, such as Embedding capacity, MSE, PSNR, and NC. The proposed method provide 14.6%, 17.34%, 19.53%, 21.46% and 23.89% high PSNR for cold-snow-landscape-water test image, 14.29%, 16.47%, 18.39%, 20.16% and 21.93% high PSNR for landscape-nature-sky-blue Test image, 16.85%, 19.99%, 22.70%, 27.22% and 29.16% high Embedding Capacity for cold-snow-landscape-water test image 22.83%, 24.64%, 27.92%, 29.60% and 31.77% high Embedding Capacity for landscape-nature-sky-blue Test image 35.38%, 32.63%, 30.95%, 28.61% and 26.08% low extraction time compared with existing methods SVD-CMSF-SIW, FE-IWS-DNN, AR-IWS-DNN, BBET-SHA1-SIW and LSB-DWT-SIW respectively.

Exploiting number theory for dynamic software watermarking

Software watermarking is a protection technique which aims at combating software piracy, thus defending intellectual property, by embedding stegosignatures or watermarks into a program. In the presence of an illegal copy of the program, the owner can reliably claim her rights by extracting the watermark from the program itself. In this paper, we introduce a new software watermarking technique which can be used even with compiled programs. The proposed technique is dynamic: the watermark can be recovered during the execution of the program and it is related to a specific path of execution that is triggered by a given input. We illustrate the technique by means of a watermarking protocol for C/C++/C# source code which tackles many different challenges in the task of encoding the watermark, embedding it into a source code and extracting it from a compiled program. We show the validity of our approach by proving its robustness against common attacks.

Review of Image Forensic Techniques Based on Deep Learning

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

Image watermarking techniques based on Schur decomposition and various image invariant moments: a review

Image watermarking techniques based on Schur decomposition and various image invariant moments: a review

An effective digital image watermarking scheme incorporating DCT, DFT and SVD transformations.

Image watermarking prevents copyright infringements by attaching visible/invisible watermark images as authentication identities in the owner's documents. The article made analysis on the advantages of different transformations for choosing better combinations to make the watermark embedding process and observed that watermarking techniques incorporating discrete cosine transform (DCT) provide better resistance towards JPEG based potential attacks, discrete Fourier transform (DFT) has strong energy compaction with geometrical invariance properties to resist geometric attacks while singular value decomposition (SVD) provides stability, proportion invariance and rotation invariance properties and it provides strong resistance against noise based attacks. Considering these advantages of different transformations, the article presents a new non-blind watermarking algorithm by utilizing advantages of DFT, DCT and SVD transforms while attaching secret contents in cover images. The algorithm starts by applying DFT followed by onion peel decomposition (OPD) for decomposing Fourier domain carrier image to four frequency sub images. The scheme then applies DCT on the frequency bands and orders them in zigzag fashion to form four individual frequency arrays. In the final step of embedding process, it embeds four copies of watermark singular value contents in DFT domain with the carrier image singular value contents to produce the watermarked image. The experimental results based on subjective and objective metrics on various test images from standard image databases with different test conditions demarcate the stability of new algorithm in producing high quality watermarked images with fewer distortions even when the watermarked images are extremely distorted by potential attacks.

Entropy based adaptive color image watermarking technique in $$YC_bC_r$$ color space

Entropy based adaptive color image watermarking technique in $$YC_bC_r$$ color space

Hardware Obfuscation Based Watermarking Technique for IPR Ownership Identification

As the reuse of IP cores or the development of frequently used hardware modules is gaining more attention in the semiconductor industry, the misappropriation of the owner’s identity is a rising concern. Therefore, imprinting the owner’s identity in the form of a watermark or signature on the IP core is essential to avoid intellectual property right (IPR) infringement. In view of this, a watermarking technique is proposed in the present manuscript. A constraint-based dynamic watermarking method to generate the owner’s signature is proposed in conjunction with the logic encryption-based hardware obfuscation method. The method formulated in this manuscript consciously makes use of a basic switching component for embedding a watermark with IP core and hardware obfuscation, to achieve a lower overhead budget. Through the switching mechanism, the embedded watermark can be made detectable to legitimate end users off chip via test pin. The logic encryption-based method is set for accessing the watermark. Furthermore, an encrypted functionality is set as the signature generator module for generating owner’s signature. This provides hardware obfuscation and two-stage authentication mechanism for the generation of owner’s signature, and as a result of this, double-layer protection is achieved. Furthermore, a novel method to configure input key for signature generation module and to formulate owner’s signature is proposed. The viability of the present watermark technique for real-life application is checked on the ground of transparency, security, reliability, performance overhead, and robustness. Since the watermark in the proposed method is embedded outside the IP core, it does not cause any latency for the IP core functionality. Thus, even with significantly lower area overhead (∼<1.4%), the proposed method is able to provide higher robustness in terms of lower probability of coincidence (PC = 4.68 e − 97).

Enhancing Biometric Fingerprint Security Through Integrated Watermarking and Cipher Block Chaining Techniques

Enhancing Biometric Fingerprint Security Through Integrated Watermarking and Cipher Block Chaining Techniques

Efficient large invisible color watermark embedding using conditional deep autoencoder model for medical applications

The widespread availability of the internet, in conjunction with very low-cost digital recording and storage devices, has ushered in an age in which the reproduction, illegal use, and malicious dissemination of digital information have become much more straightforward. Authentication of multimedia materials has garnered a lot of interest in recent days as a means of preventing illegal usage, theft, and misrepresentation of the content. Invisible watermarking tries to hide information in a medium in order to demonstrate ownership, integrity, or to hide a secret message. The goal of invisible watermarking is to hide the watermark and extract it without making it evident that the cover image is watermarked. This paper presents an invisible watermarking technique that can hide large color water marks in the cover media. The proposed model uses a Conditional Variational Autoencoder (CVAE) to encode the watermark into the cover image. The watermarked image is then decoded at the receiver to extract the watermark. Unlike the conventional watermarking techniques which hide a simple, small black and white image as watermark, the proposed model can embed large full color images as watermarks into the cover images. This makes the proposed model superior to the existing models by hiding large watermarks into the color images. The stego images produced a high PSNR greater than 40 for different watermarks and the images are visually indistinct from the original cover images.

Poisoning-Based Backdoor Attacks in Computer Vision

Recent studies demonstrated that the training process of deep neural networks (DNNs) is vulnerable to backdoor attacks if third-party training resources (e.g., samples) are adopted. Specifically, the adversaries intend to embed hidden backdoors into DNNs, where the backdoor can be activated by pre-defined trigger patterns and leading malicious model predictions. My dissertation focuses on poisoning-based backdoor attacks in computer vision. Firstly, I study and propose more stealthy and effective attacks against image classification tasks in both physical and digital spaces. Secondly, I reveal the backdoor threats in visual object tracking, which is representative of critical video-related tasks. Thirdly, I explore how to exploit backdoor attacks as watermark techniques for positive purposes. I design a Python toolbox (i.e., BackdoorBox) that implements representative and advanced backdoor attacks and defenses under a unified and flexible framework, based on which to provide a comprehensive benchmark of existing methods at the end.

Deep learning-based image watermarking technique with hybrid DWT-SVD

ABSTRACT In digital media copyright protection, image watermarking topologies afford a promising solution. But the robustness of watermarking methods should be considered. Therefore, a robust image watermarking technique has been proposed to show better robustness against rotation attacks and other issues in watermarking. In this work, the Deep Belief Network (DBN) is trained by the Bear Smell Search Algorithm (BSSA) during the first phase. The second phase is the embedding phase, which utilizes the hybrid transform domain of Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). After embedding, the extraction is performed with the help of a Back Propagation Neural Network (BPNN). The experiments are conducted on four kinds of host images such as Barbara, Peppers, Person and Lena. Experimental results proved that the superiority of our proposed method achieving the state-of-the-art accuracy.

White Box Watermarking for Convolution Layers in Fine-Tuning Model Using the Constant Weight Code.

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.

Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain

For copyright protection of diffusion-weighted imaging (DWI) images, traditional robust watermarking techniques result in irreversible distortions, while reversible watermarking methods exhibit poor robustness. We propose a two-stage lossless watermarking algorithm based on a Transformer network to solve this problem. The first stage of the algorithm is to train the robust watermarking network, embed the watermark into the cover image in the wavelet domain, and design the frequency information enhancement module to improve the reconstruction quality. In the second stage, based on the pre-trained robust watermarking network, the difference image between the watermarked image and the cover image is reversibly embedded into the watermarked image as the compensation information to losslessly recover the cover image. The difference image is compressed using DCT and Huffman coding to reduce the compensation information. Finally, the watermark extraction network is trained on the second embedding result to avoid weakening the robustness of the first stage caused by the reversible embedding. The experimental results demonstrate that the PSNR of the watermarked image reaches 60.18 dB. Under various types of image attacks, the watermark extraction BER is below 0.003, indicating excellent robustness. The cover image can be recovered losslessly under no attack.

Artificial intelligence, text generation tools and ChatGPT – does digital watermarking offer a solution?

Text generation tools, often presented as a form of generative artificial intelligence, have the potential to pose a threat to the integrity of the educational system. They can be misused to afford students marks and qualifications that they do not deserve. The emergence of recent tools, such as ChatGPT, appear to have left the educational community unprepared, despite the fact that the computer science community has been working to develop and improve such tools for years. This paper provides an introduction to text generation tools intended for a non-specialist audience, discussing the types of assessments that students can outsource, showing the type of prompts that can be used to generate text, and illustrating one possible watermarking technique that may allow generated text to be detected. A small-scale study into watermarking suggests that this technique is feasible and show technical promise but should not be relied on as a solution to widespread use of artificial intelligence based tools by students. Alternative solutions are needed, including encouraging the educational community to work with artificial intelligence rather than against it. As such, the paper concludes by discussing seven potential areas for further exploration.