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.

Survey of robust and imperceptible watermarking

An accurate diagnosis is important for the medical treatment of patients suffered from brain disease Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier The results over 64 images show that the sensitivity of the classifier is as high as 9450%, the specificity 9170%, the overall accuracy 9260% It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods

Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier

The art and science of hiding information by embedding messages in various type of digital media is called steganography. This method performs by replacing bits of multimedia files (such as graphics, sounds and texts) with bits of secret message. The information that embeds in the cover media can be plain text, cipher text and even images. Steganography sometimes is used when encryption is not permitted or more commonly, steganography is supplement for encryption. By using steganography, if the encrypted file is deciphered, the encrypted file may still hide information. This paper is proposed a new transform domain steganography method based on integer wavelet transform (IWT) for digital images and also it used a chaotic map. This map is a modified logistic map which it increases the key length and security of proposed method. Experimental results shows that the proposed method has high capability in hiding information in any images which used as a cover media. Visual quality of image after embedding process is desirable due to Peak Signal-to-Noise Ratio (PSNR) measures. Also the NIST, DIEHARD and ENT tests suite show the proposed chaotic map randomness and sensitivity to smallest changes.

A new transform domain steganography based on modified logistic chaotic map for color images

ECG steganography is performed using DWT-SVD and quantization watermarking scheme.Imperceptibility-robustness tradeoff is investigated.Continuous Ant Colony Optimization provides optimized Multiple Scaling Factors.MSFs are superior to SSF in providing better imperceptibility-robustness tradeoff. 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.

Imperceptibility-Robustness tradeoff studies for ECG steganography using Continuous Ant Colony Optimization

ECG steganography using curvelet transform

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.

Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission

Biomedical signals transmitted over the internet are usually tagged with patient information. Data hiding techniques such as steganography ensures the security of such data by hiding the data into signals. However, data hiding results in signal deterioration that might affect diagnosability. A novel technique which uses curvelet transforms to hide patient information into their ECG signal is presented. Curvelet transform decomposes the ECG signal into frequency sub-bands. A quantisation approach is used to embed patient data into coefficients whose values are around zero, in the high-frequency sub-band. Performance metrics provide the measure of watermark imperceptibility of the proposed approach. BER is used to measure the ability to extract patient data. The proposed approach is demonstrated on the MIT-BIH database and the observations validate that its performance is superior compared with the random locations approach. Although the performance of the proposed approach decreases as patient information size increases, the peak signal-to-noise ratio values are high. Therefore, the proposed approach can be used for the safe transfer of patient data.

Curvelets-based ECG steganography for data security

In concepts such as connected health, patient data needs to be protected when they are transmitted over the internet to the caregiver. The current letter proposes embedding the QR code of patient data into discrete wavelet transform coefficients of the cover ECG Signal. The imperceptibility of hidden data is estimated by performance metrics such as peak signal-to-noise ratio, percentage residual difference, and Kullback–Leibler distance. The proposed approach is demonstrated on MIT-BIH database. Watermarking in the low coefficient values deteriorate the signal less and results in lower bit errors. It is shown that larger watermarks can be accommodated with a larger version of QR code for more or less the same imperceptibility. It is found that QR code version 40 allows embedding of maximum 2632 bytes of patient data with zero bit errors. Since QR code has an inbuilt error correction code, it provides additional layer of security to patient data and the proposed approach can be used for secure transfer of patient data.

S. Edward Jero

Papers

Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission

ECG steganography using curvelet transform

Curvelets-based ECG steganography for data security

QR Code-Based Highly Secure ECG Steganography

Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals