Watermarking Scheme Research Articles

Online Signature Watermarking in the Transform Domain

The increasing reliance on digital signatures for secure authentication and verification necessitates advanced watermarking techniques to protect signature integrity. Transform-domain methods, including the discrete cosine transform (DCT) and the discrete wavelet transform (DWT), are proposed for their potential to balance robustness, imperceptibility, and recognition accuracy in online signature biometrics. This study explores multi-bit watermarking approaches applied to online handwritten signatures using the MCYT signature database. We investigate the effects of embedding multiple bits per sample with adjustable watermark strength (α) in the DCT and DWT domains. The trade-offs between signal distortion, watermark extraction accuracy, and biometric recognition rates are systematically evaluated. Experimental results reveal that while increasing α enhances watermark robustness, it also leads to perceptible distortions in signature samples. We identify the minimum α thresholds required for error-free watermark extraction and analyze their impact on identification and verification performance. The proposed multi-bit embedding strategy in the DCT domain demonstrates a viable compromise between robustness and imperceptibility, maintaining acceptable biometric recognition rates. Transform-domain watermarking techniques provide a promising solution for secure and robust online signature biometrics. This study highlights the feasibility of incorporating multi-bit watermarking schemes with adjustable strength into online signature systems, enhancing security while preserving recognition accuracy.

Secure and flexible image watermarking using IWT, SVD, and chaos models for robustness and imperceptibility

The utilization of Singular Value Decomposition (SVD) is widespread in several image watermarking methods to attain a more favorable balance between the demands of watermarking. Nevertheless, SVD exhibits some limitations, including false positives, limited capacity, and security issues. In order to overcome these challenges, a new approach to image watermarking has been introduced, which utilizes a combination of Integer Wavelet Transform (IWT), SVD, and chaos models. The generation of chaotic orbits in the scheme is based on utilizing chaotic variables generated through a secret key. The secret key is a major element in embedding, extraction, encryption, and ownership protection. The smooth generation of multiple scaling factors (MSF) matrices is achieved using chaotic points in the encryption stage. In order to attain a suitable equilibrium between watermarking robustness, imperceptibility, and security, the IWT technique is utilized to convert both the host and watermark images into frequency coefficients. The proposed method involves the extraction of a 4-bit matrix from the LL1 sub-bands of the host image. This matrix is subsequently subjected to scrambling and merged with the watermark image, resulting in a 12-bit watermark image. Afterwards, the two sub-bands of the host image undergo two levels of IWT, and three sub-bands are chosen from the resultant 2-level image for embedding. The selected sub-bands are then subjected to SVD, where the embedding process utilizes the singular values Si. The high-frequency sub-bands of the watermark image are embedded into the singular values Si of the host image using chaotic MSF matrices. The scheme also encrypts the side information to achieve high security and protect the ownership. Even a slight change in the secret key can highly affect the proposed scheme, resulting in the generation of a noise image if an incorrect key is used. This feature makes the proposed scheme robust against false positive problem (FPP) attacks. Additionally, the proposed scheme can embed watermark images with high flexibility and low distortion, achieving high robustness and imperceptibility under different hybrid MSF matrices, outperforming several other schemes. With these features, the proposed scheme successfully fulfils the security and capacity requirements of a blind watermarking scheme, making it suitable for practical watermarking applications.

Quantum watermarking scheme based on a novel geometric transformation and Fibonacci scrambling

Quantum watermarking scheme based on a novel geometric transformation and Fibonacci scrambling

Gaussian Iterated Map-Lyrebird Optimization Algorithm (GIM-LOA) and SPIHT Encoding Based Blind Dual Watermarking Scheme for Image

A prominent area of study in digital Multimediaresearch is the Digital Watermarking (DWM) method, which has shown promise as a copyright protection tool. Blind watermarking schema and non-blind Water mark schema are the two classifications under which watermarking algorithms fall. The dual blind WaterMark (WM) technique in this work employs the Stucki Kernel halftone (SKH) algorithm, Set Partitioning in Hierarchical Trees (SPIHT), and selfrecoveries in images to detect tampering. and the Gaussian Iterated Map-Lyrebird Optimization Algorithm (GIM-LOA). SPHIT is used to rebuild the image. SPHIT can produce a bitstreams at desired rates including coefficientsof Wavelet Transforms. In the event that the received bit stream is interrupted, sound progressive transmission can be used to reconstruct the image. GIM-LOA is introduced for watermark bit mapping in an iteration-based method, utilizing its members' searching skills in water embedding. Each lyrebird, as a member of the LOA, determines the values of the Watermark bits according to its bit position. Updating the optimal bit solution should also be made easier by comparing the Objective Function (OF) values and authentication bit are generated using recovery bits prior to inserting the watermark, shuffle the WM components using GIM-LOA and Arnold Cat Mapping (ACM) and thus improving security and quality of the image recovery. The suggested algorithm performs better than current methods in terms of many statistical metrics and security elements of Tampering Detection (TD) techniques, and it is protected against various attacks.

Vowel based Speech Watermarking Techniques using FFT and Min Algorithm

The critical challenge with the continuously increasing number of Internet users is copying and duplication, which has caused content integrity and protection. To manage and secure the signals from unauthorized consumers of digital content, we require certain procedures. Digital watermarking scheme on vowel-based approach can address these problems. Thus, we can provide a robust and secure method that solves the issues of copyright, illegal intentional or unintentional modification. In this paper, we have proposed vowel-based speech watermarking techniques using the FFT method with the help of the Min algorithm. We observe that the proposed FFT-based watermarking scheme provides better results in comparison to the existing methods.

Class-Hidden Client-Side Watermarking in Federated Learning.

Federated learning consists of a central aggregator and multiple clients, forming a distributed structure that effectively protects data privacy. However, since all participants can access the global model, the risk of model leakage increases, especially when unreliable participants are involved. To safeguard model copyright while enhancing the robustness and secrecy of the watermark, this paper proposes a client-side watermarking scheme. Specifically, the proposed method introduces an additional watermark class, expanding the output layer of the client model into an N+1-class classifier. The client's local model is then trained using both the watermark dataset and the local dataset. Notably, before uploading to the server, the parameters of the watermark class are removed from the output layer and stored locally. Additionally, the client uploads amplified parameters to address the potential weakening of the watermark during the aggregation. After aggregation, the global model is distributed to the clients for local training. Through multiple rounds of iteration, the saved watermark parameters are continuously updated until the global model converges. On the MNIST, CIFAR-100, and CIFAR-10 datasets, the watermark detection rates on VGG-16 and ResNet-18 reached 100%. Furthermore, extensive experiments demonstrate that this method has minimal impact on model performance and exhibits strong robustness against pruning and fine-tuning attacks.

DiffusionShield: A Watermark for Data Copyright Protection against Generative Diffusion Models

Recently, Generative Diffusion Models (GDMs) have shown remarkable abilities in learning and generating images, fostering a large community of GDMs. However, the unrestricted proliferation has raised serious concerns on copyright issues. For example, artists become concerned that GDMs could effortlessly replicate their unique artworks without permission. In response to these challenges, we introduce a novel watermark scheme, Diffusion Shield, against GDMs. It protects images from infringement by encoding the ownership message into an imperceptible watermark and injecting it into images. This watermark can be easily learned by GDMs and will be reproduced in generated images. By detecting the watermark in generated images, the infringement can be exposed with evidence. Benefiting from the uniformity of the watermarks and the joint optimization method, Diffusion Shield ensures low distortion of the original image, high watermark detection performance, and lengthy encoded messages. We conduct rigorous and comprehensive experiments to show its effectiveness in defending against infringement by GDMs and its superiority over traditional watermark methods.

TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models

Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). However, their widespread adoption raises concerns about intellectual property (IP) protection, as unauthorized use can undermine innovation. Watermarking has emerged as a promising solution for model ownership verification, but its application to NLP models presents unique challenges, particularly in ensuring robustness against fine-tuning and preventing interference with downstream tasks. This paper presents a novel watermarking scheme, TIBW (Task-Independent Backdoor Watermarking), that embeds robust, task-independent backdoor watermarks into pre-trained language models. By implementing a Trigger–Target Word Pair Search Algorithm that selects trigger–target word pairs with maximal semantic dissimilarity, our approach ensures that the watermark remains effective even after extensive fine-tuning. Additionally, we introduce Parameter Relationship Embedding (PRE) to subtly modify the model’s embedding layer, reinforcing the association between trigger and target words without degrading the model performance. We also design a comprehensive watermark verification process that evaluates task behavior consistency, quantified by the Watermark Embedding Success Rate (WESR). Our experiments across five benchmark NLP tasks demonstrate that the proposed watermarking method maintains a near-baseline performance on clean inputs while achieving a high WESR, outperforming existing baselines in both robustness and stealthiness. Furthermore, the watermark persists reliably even after additional fine-tuning, highlighting its resilience against potential watermark removal attempts. This work provides a secure and reliable IP protection mechanism for NLP models, ensuring watermark integrity across diverse applications.

Publicly-Detectable Watermarking for Language Models

We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LM output using rejection sampling and prove that this produces unforgeable and distortion-free (i.e., undetectable without access to the public key) text output. We make use of error-correction to overcome periods of low entropy, a barrier for all prior watermarking schemes. We implement our scheme and find that our formal claims are met in practice.

Proposal of an optimum method of audio steganography to secure data transfer

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

Double Security Level Protection Based on Chaotic Maps and SVD for Medical Images

The widespread distribution of medical images in smart healthcare systems will cause privacy concerns. The unauthorized sharing of decrypted medical images remains uncontrollable, though image encryption can discourage privacy disclosure. This research proposes a double-level security scheme for medical images to overcome this problem. The proposed joint encryption and watermarking scheme is based on singular-value decomposition (SVD) and chaotic maps. First, three different random sequences are used to encrypt the LL subband in the discrete wavelet transform (DWT) domain; then, HL and LH sub-bands are embedded with watermark information; in the end, we obtain the watermarked and encrypted image with the inverse DWT (IDWT) transform. In this study, SVD is used for watermarking and encryption in the DWT domain. The main originality is that decryption and watermark extraction can be performed separately. Experimental results demonstrate the superiority of the proposed method in key spaces (10225), PSNR (76.2543), and UACI (0.3329). In this implementation, the following key achievements are attained. First, our scheme can meet requests of different security levels. Second, encryption and watermarking can be performed separately. Third, the watermark can be detected in the encrypted domain. Thus, experiment results and security analysis demonstrate the effectiveness of the proposed scheme.

An Enhanced DWT-SVD-Based Robust Image Watermarking Scheme Optimized for Imperceptibility and Robustness

An Enhanced DWT-SVD-Based Robust Image Watermarking Scheme Optimized for Imperceptibility and Robustness

A Novel Deep Learning Based Dual Watermarking System for Securing Healthcare Data

ABSTRACTThe sharing of patient information on an open network has drawn attention to the healthcare system. Security is the primary issue while sharing documents online. Thus, a dual watermarking technique has been developed to improve the security of shared data. The classical watermarking schemes are resilient to many attacks. Protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust, novel dual watermarking system for securing healthcare data. Initially, watermarking is performed based on redundant lifting wavelet transform (LWT) and turbo code decomposition for COVID‐19 patient images and patient text data. To achieve a high level of authenticity, watermarks in the form of encoded text data and decomposed watermark images are inserted together, and an inverse LWT is used to generate an initial watermarked image. Improve imperceptibility and robustness by incorporating the cover image into the watermarked image. Cross‐guided bilateral filtering (CG_BF) improves cover image quality, while the integrated Walsh–Hadamard transform (IWHT) extracts features. The novel adaptive coati optimization (ACO) technique is used to identify the ideal location for the watermarked image in the cover image. To improve security, the watermarked image is dissected using discrete wavelet transform (DWT) and encrypted with a chaotic extended logistic system. Finally, the encrypted watermarked image is implanted in the desired place using a novel deep‐learning model based on the Hybrid Convolutional Cascaded Capsule Network (HC3Net). Thus, the secured watermarked image is obtained, and the watermark and text data are extracted using the decryption and inverse DWT procedure. The performance of the proposed method is evaluated using accuracy, peak signal‐to‐noise ratio (PSNR), NC, and other metrics. The proposed method achieved an accuracy of 99.26%, which is greater than the existing methods.

STEGA BLEND SECURITY

Digital asset management systems (DAMS) handle different media data in a compressed and encrypted form. It is essential to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. Attempting steganography technique is such a randomized bit stream can cause a poor degradation of the media quality. Thus it is important to choose an encryption scheme that is both secure and will allow watermarking in a predictable manner in the compressed encrypted domain. we propose a rainbow algorithm to watermark images and documents. We suggest using a stream cipher as the encryption algorithm. Although the suggested method embeds the watermark in the compressed-encrypted domain, the decrypted domain can be used to extract the watermark. Using the following watermarking schemes, we thoroughly examine the suggested algorithm's embedding capability, robustness, perceptual quality, and security: Spread Spectrum (SS), Scalar Costa Scheme Quantization Index Modulation (SCS-QIM), and Rational Dither Modulation (RDM). KEYWORDS: Encryption, Steganography techniques, Rainbow Algorithm.

C³shartMark: A Chart Watermarking Scheme With Consecutive-Encoding and Concurrent-Decoding

C³shartMark: A Chart Watermarking Scheme With Consecutive-Encoding and Concurrent-Decoding

The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields.

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness.

An Adaptive Blind Color Watermarking Scheme Based on Hadamard Transform and Information Mapping System

An Adaptive Blind Color Watermarking Scheme Based on Hadamard Transform and Information Mapping System

VUF-MIWS: A Visible and User-Friendly Watermarking Scheme for Medical Images

The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications.

Dual medical image watermarking using SRU-enhanced network and EICC chaotic map

With the rapid advancement of next-generation information technology, smart healthcare has seamlessly integrated into various facets of people’s daily routines. Accordingly, enhancing the integrity and security of medical images has gained significant prominence as a crucial research trajectory. In this study, a dual watermarking scheme based on SRU-ConvNeXt V2 (SCNeXt) model and exponential iterative-cubic-cosine (EICC) chaotic map is proposed for medical image integrity verification, tamper localization, and copyright protection. A logo image for integrity verification is embedded into the region of interest within the medical image, and a text image containing copyright information is combined with the feature vectors extracted by SCNeXt for generating zero-watermark information. The security of watermarks is strengthened through a pre-embedding encryption algorithm using the chaotic sequence produced by the EICC map. A comprehensive set of experiments was conducted to validate the proposed dual watermarking scheme. The results demonstrate that the scheme offers significant advantages in both imperceptibility and robustness over traditional methods, including those that rely on manual extraction of medical image features. The scheme achieves excellent imperceptibility, with an average PSNR of 52.29 dB and an average SSIM of 0.9962. Moreover, it displays strong resilience against various attacks, particularly high-strength common and geometric attacks, maintaining an NC value above 0.84, which confirms its robustness. These findings highlight the superiority of the proposed dual watermarking scheme, establishing its potential as an advanced solution for secure and reliable medical image management.

Image Watermark Embedding Method Based on Security Service Secure

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent