Effective Electrocardiogram Steganography Based on Coefficient Alignment

Yang, Ching-Yu; Wang, Wen-Fong

doi:10.1007/s10916-015-0426-9

Cited by 37 publications

(31 citation statements)

References 18 publications

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“…However, only a few authors have focused on embedding secret messages in ECG signals or healthcare systems [12][13][14]. Data-hiding methods, i.e., ECG steganography approaches, have been applied to ECG signals for securing patient information [15][16][17][18][19][20][21][22][23][24][25][26][27]. To protect against manipulations, researchers have presented schemes for digital watermarking of ECG signals [28][29][30][31].…”

Section: Overview Of Relevant Workmentioning

confidence: 99%

“…Wang et al [25] proposed two methods to perform ECG reversible data hiding. Based on coefficient alignment [26], two kinds of ECG steganographic methods were proposed. Regardless of the aforementioned steganography techniques, the main goal that each method must achieve high embedding capacity given a fixed distortion constraint between the cover and stego signal while allowing reliable retrieval of steganographed data subject to a fixed distortion between embedded and extracted data.…”

Section: Overview Of Relevant Workmentioning

confidence: 99%

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An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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Evolving digital technologies in remote health monitoring require an energy-efficient method for secure and reliable transmission of patient's/user's confidential information from the sensor nodes to the cloud/medical server. Thus, a united scheme of the physiological signal steganography and its communication by benefitting from the unequal significance between different parts of the physiological data are emphasized. We believe higher steganography coding strength and more robust source-channel coding would protect extremely vital parts of the physiological data. Therefore, data integrity and transmission efficiency of packet information achieved in a resilient way. We formulate our idea of joint steganography-source-channel coding (JS 2 C 2) as an optimization problem to simultaneously securing and minimizing the transmission energy consumption. A low-complexity deep learning-based ECG classification algorithm along with its secure and energy-efficient neural JS 2 C 2 transmission for real-time monitoring has been realized. The optimal parameters for our united framework have been calculated by JS 2 C 2 optimization method. Our steganography algorithm unequal steganography embedding (USE) achieves very low wavelet-based weighted percent root-mean-squared difference lower than 0.5%. Furthermore, the high correlation between cover and stego and low end-to-end mean-square error (MSE) indicates resilient imperceptibility and maintains the diagnosability of the physiological signal. Moreover, low MSE between embedded and extracted data validates that embedded confidential data has been extracted with negligible distortion. In addition, for the given distortion, the USE-based framework's energy consumption is much smaller (by 55% in typical application scenario) as compared with the equal steganography embedding-based approach's energy consumption. Keywords Electrocardiography (ECG) • Unequal error protection (UEP) • Unequal steganography embedding (USE) • Joint steganography source coding (JS 2) • Joint source-channel coding (JSC 2) • Joint steganography-source-channel coding (JS 2 C 2) • Deep learning (DL) • Security • Correlation • End-to-end distortion • Energy efficiency

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Section: Overview Of Relevant Workmentioning

confidence: 99%

Section: Overview Of Relevant Workmentioning

confidence: 99%

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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“…Two coefficient-alignment based ECG data hiding mechanism is proposed in [14]. Unlike most of the ECG data hiding mechanisms, in here, the authors have embedded the watermarks directly into the ECG samples in the time domain.…”

Section: A Data Hiding Methods Developed For Ecg Signalsmentioning

confidence: 99%

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

et al. 2020

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At present, a patient's demography, such as name, age, and gender are stored separately from the acquired electrocardiogram (ECG) signal. This multiple storage mechanisms can create a severe threat to the reliability of diagnostics if the link between the demography data and the ECG signal breaks, either intentionally or unintentionally. This issue has become more prominent in recent years due to the use of a large number of wearable devices for physiological signal collection, especially in remote or nonclinical settings. In order to address this problem, in this paper, we propose a novel mechanism to embed patient's information within an ECG signal without degrading the accuracy of the physiological information contained in the ECG signal. In this work, a methodology is presented to find the less-significant region of the ECG signal. Then, the patient information is hidden in this region by modifying the selected discrete cosine transform (DCT) coefficients of the signal using our proposed embedding and decoding algorithms. Moreover, the patient information hidden in the ECG signal is able to resist filtering attack, such as high-pass filtering, which generally occur with the ECG signal processing. This is achieved via the use of error buffers in the embedding algorithm. The proposed mechanism can extract the embedded patient information, either in the presence or without the filtering attack. Moreover, a specifically designed synchronization sequence is added to identify the patient data embedded regions of the ECG signal at the decoding end. Further, as a security measure, the embedded patient details are scrambled using a secret key to protect the privacy of the patient. Our evaluation demonstrates the usefulness of the proposed methodology in successfully embedding the patient information without distorting the important medical information in an ECG signal. INDEX TERMS ECG signal, information hiding, watermarking, physiological signal processing, discrete cosine transform.

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“…Recently, data hiding has played a crucial role in protecting secret data in multimedia files, such as documents, images, and videos [1][2][3][4][5]. In addition, several researchers have presented data hiding for biomedical signals such as an electrocardiogram (ECG) to secure personal sensitive information, including patients' diagnoses [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Simulations confirmed that the average SNR introduced using the proposed method was approximately 42 dB with the payload of size 15,000 bits and a certain degree of robustness. Yang and Wang [12] employed a smart coefficient alignment technique and designed an effective ECG steganography for hiding patients' information. The technique has two approaches: method A and method B.…”

Section: Introductionmentioning

confidence: 99%

Progressive Data Hiding in Integer Wavelet Transform of Electrocardiogram by Using Simple Decision Rule and Coefficient Calibration

Yang¹,

Wang²

2020

RIA

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Based on one dimensional (1D) integer wavelet transform (IWT) domain, patient's data can be effectively embedded in electrocardiogram (ECG) via the proposed criterion and coefficient alignment. Multiple data bits can be sequentially hidden in host bundles (with different lengths) of the low and high subbands of IWT coefficients. Simulations indicated that the average signal-to-noise ratio (SNR) and the payload of the proposed method are superior to those of existing techniques. Additionally, the proposed method exhibited a robustness that has rarely been observed in conventional ECG steganography. Our method is capable of resisting attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise addition. Because bit embedding and extraction procedures are quite simple, our method can be applied in portable biometric devices.

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Effective Electrocardiogram Steganography Based on Coefficient Alignment

Cited by 37 publications

References 18 publications

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

Progressive Data Hiding in Integer Wavelet Transform of Electrocardiogram by Using Simple Decision Rule and Coefficient Calibration

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