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... Such risks also exist in the field of multimedia, in which one common solution is the digital watermarking technique. By inserting different watermark signals into different multimedia data, such as images [1,2] , video [3,4] , 3D mesh [5][6][7] , the copyright of which can be effectively protected. Therefore, to remedy such risk in databases, there has been some research on digital watermarking techniques for time series data [8][9][10] . ...
Database watermarking is one of the most effective methods to protect the copyright of databases. However, traditional database watermarking has a potential drawback: watermark embedding will change the distribution of data, which may affect the use and analysis of databases. Considering that most analyses are based on the statistical characteristics of the target database, keeping the consistency of the statistical characteristics is the key to ensuring analyzability. Since statistical characteristics analysis is performed in groups, compared with traditional relational databases, time series databases (TSDBs) have obvious time-grouping characteristics and are more valuable for analysis. Therefore, this paper proposes a robust watermarking algorithm for time series databases, effectively ensuring the consistency of statistical characteristics. Based on the time-group characteristics of TSDBs, we propose a three-step watermarking method, which is based on linear regression, error compensation, and watermark verification, named RCV. According to the properties of the linear regression model and error compensation, the proposed watermark method generates a series of data that have the same statistical characteristics. Then, the verification mechanism is performed to validate the generated data until it conveys the target watermark message. Compared with the existing methods, our method achieves superior robustness and preserves constant statistical properties better.r.
... H.264/AVC encoder is used to mark the samples of the signal within the motion Fig. 6 Original EEG signal [64] vectors. Duy et al. proposed another blind scheme where secret data is embedded within EEG signal using approximation coefficient values and SVDD based extraction is practiced for watermark retrieval [74]. Owner signature and logo is taken as watermark and Arnold transformation is applied to it prior embedding. ...
Due to smart healthcare systems highly connected information and communications technologies, sensitive medical information and records are easily transmitted over the networks. However, stealing of healthcare data is increasing crime every day to greatly impact on financial loss. In order to this, researchers are developing various cost-effective bio-signal based data hiding techniques for smart healthcare applications. In this paper, we first introduce various aspects of data hiding along with major properties, generic embedding and extraction process, and recent applications. This survey provides a comprehensive survey on data hiding techniques, and their new trends for solving new challenges in real-world applications. Then, we survey the various notable bio-signal based data hiding techniques. The summary of some notable techniques in terms of their objective, type of data hiding, methodology and database used, performance metrics, important features, and limitations are also presented in tabular form. At the end, we discuss the major issues and research directions to explore the promising areas for future research.
... Although the results of discussed scheme are compared with other existing methods [72,82,109]. Duy et al. proposed machine learning based watermarking method for EEG data in DWT domain [20]. The method uses intelligent learning machine for recover the watermark to save memory for storing both data (EEG and watermark). ...
Robustness, imperceptibility and embedding capacity are the preliminary requirements of any watermarking technique. However, research concluded that these requirements are difficult to achieve at same time. In this paper, we review various recent robust and imperceptible watermarking methods in spatial and transform domain. Further, the paper introduces elementary concepts of digital watermarking, characteristics and novel applications of watermark in detail. Furthermore, various analysis and comparison of different notable watermarking techniques are discussed in tabular format. We believe that our survey contribution will helpful for fledgling researchers to develop robust and imperceptible watermarking algorithms for various practical applications.
As sharing digital media is getting more prominent, there has been a rise in the development of techniques for protecting digital media against misuse. However, the amount of such techniques has been less prominent for printed goods. The data hiding method presented in this paper is suited for the print domain. The method uses the Discrete Fourier Transform to embed data into images and Gray Component Replacement to maintain high image quality by masking embedding artifacts. This research examines whether the method's performance can be optimized using low-frequency ranges for embedding. The aim is to evaluate the usage of low-frequency ranges in order to achieve higher robustness. Different frequency ranges are tested to determine how they affect image quality and detection rates. Tests are conducted in the digital domain. The results show that the method can maintain image quality regardless of the frequency range, and that low-frequency ranges lead to more consistent detection rates. Future research will be done on images in the print domain.
Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance.
The quick development and advance in information technology and computer networks have brought a more and more attention to data transmission in digital form. The main problem of owners and producers of digital products is to defend against distribution and unauthorized copying. An efficient solution for this disturbance is using the digital watermarking techniques. The purpose of audio digital watermarking is to insert a series of hidden information into audio files, so that it can’t be heard and is robust against signal processing attacks. One of the major problems of the conventional audio watermarking schemes includes the rule-based decoders which use some sets of specific rules for watermark extraction without any intelligence. In this paper, we propose a new robust intelligent audio watermarking scheme based on a collaboration of discrete wavelet transform and K-nearest neighbor (KNN) techniques. The scheme carries out the embedding of watermark data based on modifying energy levels in wavelet domain. Moreover, an intelligent KNN learning machine is trained to capture the correlation between modified frequency coefficients, in wavelet domain, and the watermark sequence. In extracting phase, the watermarked data can be effectively retrieved using the trained KNN machine. The watermarking scheme preserves data synchronization through inserting a chaotic sequence. In order to evaluate imperceptibility and robustness of the proposed watermarking scheme, several experiments under various conditions are carried out. The experimental results show a relatively better imperceptibility, higher robustness and capacity than the conventional techniques. Data embedding rate is 1600 bps.
In Wireless telecardiology applications, an ECG signal is often transmitted without any patient details which are often supplied separately as clear text. This allows the possibility of confusion of link between signal and identity (for example, with wireless signal collision attacks). ECG data transmission can be more robustly tied to either patient identity or other patient meta-data if this meta-data is embedded within the ECG signal itself when sent. In this paper ECG signals are watermarked with patient biomedical information in order to confirm patient/ECG linkage integrity. Several cases have been tested with different degrees of signal modification due to watermarking. These show its effect on the diagnostic value of the signal (for example, the PRD as an error measure). It is found that a marginal amount of signal distortion that is sufficient to hold the patient information, will not affect the overall quality of the ECG. The proposed system will not increase the size of host signals, nor change its scaling nor bandwidth. In addition, its low complexity makes it suitable for power-limited wearable computing and sensor-net applications.
In this article, we use a self-synchronized watermark technology [7], to achieve the purpose of protection of electrocardiogram (ECG) signal. A Harr wavelet transform with 7 levels decomposition is adopted to transform the ECG signal and the synchronization code, combined with watermark, are quantized embedded in the low-frequency sub-band of level 7. The signal to noise ratio (SNR) between the embedded ECG and original one is greater than 30 such that the difference between these two ECG signals is very small and negligible in general. To test the robustness under the network transfer of ECG data, a white noise attack with various strengths is simulated that the bit error rate is quite small unless the SNR of the noise is very large. This study confirms the use of wavelet-based quantization watermarking scheme on ECG signal for patient protection is adequate.
Digital watermarking is an application associated with copyright protection. Any digital object can be used as a carrier to carry information. If the information is related to object then it is known as a watermark which can be visible or invisible. In the era of digital information, there are multiple danger zones like copyright and integrity violations, of digital object. In case of any dispute during rights violation, content creator can prove ownership by recovering the watermark. Two most important prerequisites for an efficient watermarking scheme are robustness and security. Watermark must be robust and recoverable even if a part of content is altered by one or more attacks like compression, filtering, geometric distortions, resizing, etc. In this work, we propose a blind watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). Singular values (SV’s) of high frequency (HH) band are used to optimize perceptual transparency and robustness constraints. Although most of the SVD-based schemes prove to be robust, little attention has been paid to their security aspect. Therefore, we introduce a signature-based authentication mechanism at the decoder to improve security. Resulting blind watermarking scheme is secure and robust.
The large datasets are being mined to extract hidden knowledge and patterns that assist decision makers in making effective, efficient, and timely decisions in an ever increasing competitive world. This type of “knowledge-driven” data mining activity is not possible without sharing the “datasets” between their owners and data mining experts (or corporations); as a consequence, protecting ownership (by embedding a watermark) on the datasets is becoming relevant. The most important challenge in watermarking (to be mined) datasets is: how to preserve knowledge in features or attributes? Usually, an owner needs to manually define “Usability constraints” for each type of dataset to preserve the contained knowledge. The major contribution of this paper is a novel formal model that facilitates a data owner to define usability constraints-to preserve the knowledge contained in the dataset-in an automated fashion. The model aims at preserving “classification potential” of each feature and other major characteristics of datasets that play an important role during the mining process of data; as a result, learning statistics and decision-making rules also remain intact. We have implemented our model and integrated it with a new watermark embedding algorithm to prove that the inserted watermark not only preserves the knowledge contained in a dataset but also significantly enhances watermark security compared with existing techniques. We have tested our model on 25 different data-mining datasets to show its efficacy, effectiveness, and the ability to adapt and generalize.
We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.
Digital watermarking has been proposed to increase medical image security, confidentiality and integrity. Med-ical image watermarking is a special subcategory of im-age watermarking in the sense that the images have special requirements. Particularly, watermarked medical images should not differ perceptually from their original counter-parts, because the clinical reading of the images (e.g. for diagnosis) must not be affected. This paper presents a pre-liminary study on the degradation of medical images when embedded with different watermarks, using a variety of pop-ular systems. Image quality is measured with a number of widely used metrics, which have been applied elsewhere in image processing. The general conclusion that arises from the results is that typical watermark embedding can cause numerical and perceptual errors in an image. The greater the robustness of a watermark, the greater the errors are likely to be. Consequently medical image watermarking re-mains an open area for research, and it appears that a se-lection of different watermarks for different medical image types is the most appropriate solution to the generic prob-lem.
Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.
Recently, a significant interest has been developed in motivating physicians to use Electronic Medical Records (EMR) systems. An important utility of such EMR systems is: a next generation of Clinical Decision Support Systems (CDSS) will extract knowledge from the EMR to enable physicians to do accurate and effective diagnosis. In future such medical records will be shared through cloud among different physicians to improve the quality of health care. Therefore, their right protection is important to protect their ownership once they are shared with third parties. Watermarking is a proven well known technique to achieve this objective. The challenges associated with watermarking of EMR systems are: (1) some fields in EMR are more relevant in the diagnosis process; therefore, small variations in them could change the diagnosis, and (2) a misdiagnosis might not only result in a life threatening scenario but also leads to significant costs of the treatment to the patients. The major contribution of this paper is an information-preserving watermarking scheme to address the above-mentioned challenges. Our pilot studies reveal that our scheme never degrades the classification accuracy by more than 1%. In comparison, a well known threshold-based technique degrades the accuracy by more than 18%.
The demand for the secondary use of medical data is increasing steadily to allow for the provision of better quality health care. Two important issues pertaining to this sharing of data have to be addressed: one is the privacy protection for individuals referred to in the data; the other is copyright protection over the data. In this paper, we present a unified framework that seamlessly combines techniques of binning and digital watermarking to attain the dual goals of privacy and copyright protection. Our binning method is built upon an earlier approach of generalization and suppression by allowing a broader concept of generalization. To ensure data usefulness, we propose constraining binning by usage metrics that define maximal allowable information loss, and the metrics can be enforced off-line. Our watermarking algorithm watermarks the binned data in a hierarchical manner by leveraging on the very nature of the data. The method is resilient to the generalization attack that is specific to the binned data, as well as other attacks intended to destroy the inserted mark. We prove that watermarking could not adversely interfere with binning, and implemented the framework. Experiments were conducted, and the results show the robustness of the proposed framework.
Accurate and reliable information sharing is essential in the healthcare domain. Currently, however, information about individual patients is held in isolated medical records maintained by numerous separate healthcare providers. Accurately linking this information is necessary for planned nationwide Electronic Health Record systems, but this must be done in a way that not only satisfies traditional data confidentiality requirements, but also meets patientspsila personal privacy needs. Here we present an architecture for linking electronic medical records in a way that gives patients control over what information is revealed about them. This is done through the use of indirect pseudonym identifiers. We then explain how this architecture can be implemented using existing technologies. A case study is used to show how our architecture satisfies data accuracy needs and patientspsila privacy requirements.
This paper introduces a new watermarking modulation technique, which we call improved spread spectrum (ISS). When compared with traditional spread spectrum (SS), the signal does not act as a noise source, leading to significant gains. In some examples, performance improvements over SS are 20 dB in signal-to-noise ratio (SNR) or ten or more orders of magnitude in the error probability. The proposed method achieves roughly the same noise robustness gain as quantization index modulation (QIM) but without the amplitude scale sensitivity of QIM. Our proposed ISS is as robust in practice as traditional SS.
The watermarking of digital images, audio, video, and multimedia
products in general has been proposed for resolving copyright ownership
and verifying originality of content. This paper studies the
contribution of watermarking for developing protection schemes. A
general watermarking framework (GWF) is studied and the fundamental
demands are listed. The watermarking algorithms, namely watermark
generation, embedding, and detection, are analyzed and necessary
conditions for a reliable and efficient protection are stated. Although
the GWF satisfies the majority of requirements for copyright protection
and content verification, there are unsolved problems inside a pure
watermarking framework. Particular solutions, based on product
registration and related network services, are suggested to overcome
such problems
Copyright and integrity violations of digital medical data have become security challenges since the ever-increasing distribution of them between clinical centres and hospitals through the widespread usage of telemedicine, teleradiology, telediagnosis, and teleconsultation. Therefore, preserving authenticity and integrity of medical data including Electroencephalogram (EEG) has become a necessity. Watermark techniques have been thoroughly studied as a means to preserve the authenticity and integrity of the content of medical. Although there is a large volume of works on watermarking and stenography, not many researchers have addressed issues related to EEG data. This paper proposes a new approach that uses discrete wavelet transform (DWT) to decompose EEG signals and singular value decomposition (SVD) to embed watermark into the decomposed EEG signal. Based on the advantage of using the SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.
ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely and to minimize the deterioration in the cover signal caused by the embedding. This paper presents a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback–Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility or robustness.
1. Introduction to wavelets 2. Review of Fourier theory and filters 3. Orthonormal transforms of time series 4. The discrete wavelet transform 5. The maximal overlap discrete wavelet transform 6. The discrete wavelet packet transform 7. Random variables and stochastic processes 8. The wavelet variance 9. Analysis and synthesis of long memory processes 10. Wavelet-based signal estimation 11. Wavelet analysis of finite energy signals Appendix. Answers to embedded exercises References Author index Subject index.
ECG Steganography provides secured transmission of secret information such as patient personal information through ECG signals. This paper proposes an approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal. The novelty of the proposed method is to embed the watermark using SVD into the two dimensional (2D) ECG image. The embedding of secret information in a selected sub band of the decomposed ECG is achieved by replacing the singular values of the decomposed cover image by the singular values of the secret data. The performance assessment of the proposed approach allows understanding the suitable sub-band to hide secret data and the signal degradation that will affect diagnosability. Performance is measured using metrics like Kullback-Leibler divergence (KL), percentage residual difference (PRD), peak signal to noise ratio (PSNR) and bit error rate (BER). A dynamic location selection approach for embedding the singular values is also discussed. The proposed approach is demonstrated on a MIT-BIH database and the observations validate that HH is the ideal sub-band to hide data. It is also observed that the signal degradation (less than 0.6 %) is very less in the proposed approach even with the secret data being as large as the sub band size. So, it does not affect the diagnosability and is reliable to transmit patient information.
In this paper, a novel biometrics inspired watermarking technique is proposed. For this purpose, a newly proposed mathematical transform, namely the fractional dual tree complex wavelet transform (FrDT-CWT), and singular value decomposition (SVD) are used. The core idea is to use biometrically generated keys in the embedding process of a gray-scale watermark. Therefore, this paper first proposes a method for generating keys from biometrics efficiently. The host image is first randomized by Hessenberg decomposition and a chaotic map, followed by embedding in the FrDT-CWT domain by modifying the singular values of the randomized image. Further, in order to prevent the ambiguity problem of SVD-based techniques, a verification step is introduced to verify the watermarked image. Finally, a reliable extraction process is proposed to extract the watermark from the possibly attacked watermarked image after verification. The security, attack, and comparative analysis confirms the high security, efficiency, and robustness of the proposed watermarking technique.
This paper presents a secure, robust, and blind adaptive audio watermarking algorithm based on singular value decomposition (SVD) in the discrete wavelet transform domain using synchronization code. In our algorithm, a watermark is embedded by applying a quantization-index-modulation process on the singular values in the SVD of the wavelet domain blocks. The watermarked signal is perceptually similar to the original audio signal and gives high quality output. Experimental results show that the hidden watermark data is robust to additive noise, resampling, low-pass filtering, requantization, MP3 compression, cropping, echo addition, and denoising. Performance analysis of the proposed scheme shows low error probability rates. The data embedding rate of the proposed scheme is 45.9 bps. The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes.
We proposed a novel discrete fractional Sine transform (DFRST) based watermarking scheme for audio data copyright protection. Chaotic sequences were adopted to improve the security of the proposed watermarking scheme. Simulations under various conditions were given to verify the effectiveness of the audio watermarking scheme. The results show the proposed scheme is secure, and the watermark is imperceptible and robust against various audio signal processing attacks.
Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals
In this paper, we review the algorithms for rotation, scaling and translation (RST) invariant image watermarking. There are mainly two categories of RST invariant image watermarking algorithms. One is to rectify the RST transformed image before conducting watermark extraction. Another is to embed and extract watermark in an RST invariant or semi-invariant domain. In order to help readers understand, we first introduce the fundamental theories and techniques used in the existing RST invariant image watermarking algorithms. Then we discuss in detail the work principles, embedding process, and extraction process of the typical RST invariant image watermarking algorithms. Finally, we analyze these typical algorithms, and point out their advantages and disadvantages. Categories and Subject Descriptors: I. 4 [Computing Methodologies]: Image Processing General Terms: algorithms, design, performance Additional Key Words and Phrases: Digital image watermarking, RST invariant, Fourier-Mellin transform, LPM, ILPM, Radon transform, feature points, template matching, moments
Use of wireless technology has made the bio-medical data vulnerable to attacks like tampering, hacking etc. This paper proposes the use of digital watermarking to increase the security of an ECG signal transmitted through a wireless network. A low frequency chirp signal is used to embed watermark which is patient's identification taken as 15 digit code. The characteristic of the proposed watermarking scheme is that the blind recovery of the watermark is possible at the receiver and the embedded watermark can be fully removed. Hence, ECG can be viewed by a clinician with zero distortion which is an essential requirement for bio-medical data. Further, tampering such as noise addition and filtering attack can also be detected at the receiver.
Nowadays, watermarking has become a technology of choice for a broad range of multimedia copyright protection applications. Watermarks have also been used to embed prespecified data in biomedical signals. Thus, the watermarked biomedical signals being transmitted through communication are resistant to some attacks. This paper investigates discrete wavelet transform based watermarking technique for signal integrity verification in an Electrocardiogram (ECG) coming from four ECG classes for monitoring application of cardiovascular diseases. The proposed technique is evaluated under different noisy conditions for different wavelet functions. Daubechies (db2) wavelet function based technique performs better than those of Biorthogonal (bior5.5) wavelet function. For the beat-to-beat applications, all performance results belonging to four ECG classes are highly moderate.
We propose a new image cryptosystem to protect image data. It
encrypts the original image into another virtual image. Since both
original and virtual images are significant, our new cryptosystem can
confuse illegal users. Besides the camouflage, this new cryptosystem has
three other benefits. First, our cryptosystem is secure even if the
illegal users know that our virtual image is a camouflage. Second, this
cryptosystem can compress image data. Finally, our method is more
efficient than a method that encrypts the entire image directly
Wavelet-based quantization watermarking for ecg signals
- Xialong He
- Huang-Nan Kuo-Kun Tseng
- Shuo-Tsung Huang
- Shu-Yi Chen
- Fufu Tu
- Jeng-Shyang Zeng
- Pan
A proposed blind dwt-svd watermarking scheme for eeg data
- Dat Trung Duy Pham
- Wanli Tran
- Ma