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Transactions on Engineering and Computing Sciences - Vol. 11, No. 3
Publication Date: June 25, 2023
DOI:10.14738/tecs.113.14919.
Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment
Technique and Least Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
Services for Science and Education – United Kingdom
A New ECG Steganography Based on Enhanced Coefficient
Adjustment Technique and Least Significant Bit Substitution
Ching-Yu, Yang
Dept. Computer Science and Information Engineering,
National Penghu University of Science and Technology, Taiwan
Tsung-Hsiang, Hsu
Dept. Computer Science and Information Engineering,
National Penghu University of Science and Technology, Taiwan
Pei-Yun, Chen
Dept. Computer Science and Information Engineering,
National Penghu University of Science and Technology, Taiwan
ABSTRACT
A new data hiding technique was presented to conceal the privacy of patient’s
diagnosis and personal sensitive information in an electrocardiogram (ECG) signal.
Instead of hiding one data bit in a host block at a time by traditional coefficient
adjustment technique (CAT), we embed two data bits in the same size of the block
via the proposed enhanced CAT (ECAT) method. The resultant payload provided by
ECAT is nearly twice as large as that provided by the CAT scheme. In addition, if
ECAT equipped with the least significant bit (LSB) substitution, then a large number
of bits can be hidden in the ECG hosts while the resulting perceived quality is good.
Simulations revealed that both the average payload and SNR and the proposed
method are superior to those of the CAT-based schemes and existed ECG
steganographic methods. The application of our method can be found in portable
(or wearable) measurement devices.
Keywords: Data hiding, ECG steganography, CAT, ECAT, LSB.
INTRODUCTION
Due to steady growth of very high-speed backbone network service, the improving chip
manufacturing technology and ubiquitous use of powerful applications in mobile devices, plus
the maturity of artificial intelligence algorithms and 5G communication technology, people can
easily roam from the Internet and share their resources conveniently. However, data can be
eavesdropped or tampered with during transmission. Data hiding [1-3] provides a simple and
economical ways to protect (or secure) sensitive or important information. Generally speaking,
data hiding can be classified as steganography and digital watermarking [4-6]. A major goal of
steganography is to achieve hiding storage as large as possible while the resultant perceived
quality is good. Since a good perceived quality of marked media attracts no attention from the
third parties. Typical applications of steganography can be found in covert channel
communications between two parties and the hidden of private (or important) data in
multimedia. As for a major purpose of digital watermarking is to purse robustness performance
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
while maintaining acceptable perceptual quality. Namely, the watermarking schemes intend to
protect copyrights, hoping to avoid or prevent intentionally (or unintentionally) manipulating
of watermarked multimedia. Recently, researchers pay attention to hide patient’s diagnosis
and personal data in biometric measure signals such as electrocardiogram (ECG) and
Electroencephalography [7-13]. Since the objective of the proposed method is to conceal secret
message in an ECG host and improve the performance of the variants of CAT-based ECG
steganography [7,10, 12, 13], only related (lossy) methods are reviewed here.
Yang and Wang [7] used coefficient alignment and developed a high-quality and a high-capacity
ECG steganography. Simulation revealed that the average SNR/ payload were 54.00 dB/ 7,500
bits and 42.47/ 14,783 for the high-quality and high-capacity versions, respectively. To
improve previous work, Yang and Wang [10] suggested an absolute-value-decision policy to
hide secrete bits in the ECG hosts. According to the decision policy, at most two data bits can
be hidden in a host block via the adjustment of the coefficients in the block. In addition, to
obtain a high-perceived quality, a 1-bit version of the method was presented to achieve the
goal. An apparent merit of the method is to use no extra information during the bit
embedding/extraction process. Simulations showed that the average SNR (in dB)/ payload (in
bits) of the method was 47.54/ 19,614 and 57.80/ 9,982 for the 2-bit and 1-bit version of the
method, respectively. Based on two-dimensional (2D) approach, Yang et al. [12] proposed an
ECG steganography. Before conducting bit embedment, they converted input ECG data from
one-dimensional (1D) arrangement into series of 2D block with the size of 3 3. According to
the coefficient adjustment approach, six data bits can be hidden in a host block. Simulations
indicated that the average SNR of the method is superior to that of existed techniques, whereas
the payload of the method is competitive to that of the compared methods. An apparent
disadvantage of the method was the waste of residual blocks (without being used for bit
hidden) after the conversion of ECG from 1D to 2D.
Based on the integer wavelet transform (IWT), Yang and Wang [13] presented a data hiding to
hide personal message in an ECG host via the offset of IWT coefficients. To embed data bits in
the coefficients of a host block with the size of 1 n, they used the coefficient adjusting rule,
that is, |
∑i=0
n−2
sji
n−1
− sj(n−1)
| ≤ τ, where τ is a control integer, n is the size of the host block, and sji
is the ith coefficient of the jth block. Namely, a data bit 1 or 0 can be hidden in a host block of
size 2 if −τ ≤ (sj0 − sj1) < 0 or 0 ≤ (sj0 − sj1) < τ is satisfied. Subsequently, input the next
bit 1 or 0 can be embedded in the block of size 3, if either −τ ≤ (
sj0−sj1
2
− sj2) < 0 or 0 ≤
(
sj0−sj1
2
− sj2) < τ is satisfied. Theoretically, data bits (of length n − 1) can be concealed in a host
block of size n. It is obvious that a large value of n provides a large hiding space, but degrades
the perceived quality (with the lower SNR) by the method. The procedure of coefficient
adjustment has to be conducted if rule-violation occurs during bit embedding. In addition, if a
block (or sub-block) carries no data bit, it is referred to as a skipped block. No extra information
is required for the recoding of the position of skipped blocks, because the skipped blocks can
be easily detected at the receiver according to the above coefficient adjusting rule.
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PROPOSED METHOD
To improve the hiding capacity of traditional CAT [13], an enhanced ECAT was presented here
to achieve the goal. Instead of hiding two data bits in a host bundle with the size of 1 3, the
proposed ECAT method embeds four bits in a host block with the same size. Theoretically, the
coding efficiency (4 / 3) of the ECAT and is twice as large as that (2 / 3) of the CAT. Specifically,
at most four data bits can be embedded in a bundle according to the following two decision
rules, namely, R1 and R2. They are defined as follows:
R1: |xi − xi+1
| < 2τ (1)
and
R2: |
xi−xi+1
2
− xi+2| < 2τ (2)
where is a control parameter, xi, xi+1, and xi+2 are the three adjacent coefficients in a host
bundle. Without loss of generality, let α, β, γ, and δ be represented as the two bits “00,” “01,”
“10,” and “11,” which come from input bit-stream. The proposed ECAT method consists of two
phases. In phase I, α, β, γ, and δ can be “virtually” hidden into a host bundle of size 2 if R1 is
satisfied. In phase II, two data bits can be “carried” by the bundle of size 3 if R2 is satisfied. If
none of the above rules is satisfies, then the bundle carries no data bit and is referred to as a
skipped block. To further increasing hiding space, the ECAT equipped with the LSB substitution
is presented here. That is, instead of skipping the bundle (or sub-bundle), we embed data bits
in the skipped bundles by the LSB. Namely, in Phase I, we embed four data bits in the
coefficients of xi and xi+1 via the LSB as 2τ ≤ |xi− xi+1| is true. However, to avoid the altering the
values of the two coefficients (marked by Phase I), only two bits would be hidden into the
coefficient xi+2 via the LSB (in Phase II) if the condition of 2τ ≤ |
(xi+xi+1)
2
− xi+2| is satisfied. The
main steps of bit embedding and bit extraction for the proposed ECAT with the LSB method is
summarized in the following sections.
Bit Embedding
The main procedure of bit embedding is described in the following algorithm.
Algorithm 1: Embedding a secret message in an ECG host.
Input: Host ECG H, the size of a bundle n, control integer τ, and secret message W.
Output: Marked ECG Ĥ .
Method:
Step 1. Input a bundle, say Hj = {xi
}i=0
n−1
from the jth bundle of Η. If the end of input is
encountered, then go to Step 14.
Step 2. Read in two data bits ∈ {00, 01, 10, 11} from W and compute the offset η = xi − xi+1.
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
[Phase I]
Step 3. If 2τ ≤ |η| then read in next two bits o ∈ {00, 01, 10, 11} from W and embed and o
in xi and xi+1, respectively, via the LSB technique. Proceed to Step 8.
Step 4. If the conditions of = α and 0 ≤ (xi − xi+1) < τ are satisfied, then the bundle “carried”
α and go to Step 8, otherwise, if = α, then repeat adjust both coefficients xi and xi+1 either by
increment or decrement, respectively, until 0 ≤ (xi − xi+1) < τ is true and proceed to Step 8.
Step 5. If the conditions of = β and − τ ≤ (xi− xi+1) < 0 are satisfied, then the bundle “carried”
β and go to Step 8, otherwise, if = β, then repeat adjust both coefficients xi and xi+1 either by
increment or decrement, respectively, until −τ ≤ (xi − xi+1) < 0 is true and proceed to Step 8.
Step 6. If the conditions of = γ and τ ≤ (xi − xi+1) < 2τ are satisfied, then the bundle “carried”
γ and go to Step 8, otherwise, if = γ, then repeat adjust both coefficients xi and xi+1 either by
increment or decrement, respectively, until τ ≤ (xi − xi+1) < 2τ is true and proceed to Step 8.
Step 7. If the conditions of = δ and − 2τ ≤ (xi− xi+1) < τ are satisfied, then the bundle “carried”
δ and go to Step 8, otherwise, repeat adjust both coefficients xi and xi+1 either by increment or
decrement, respectively, until −2τ ≤ (xi − xi+1) < −τ is true.
[Phase II]
Step 8. Read in next two data bits ν ε{00,01,10,11} from W and compute the offset κ =
(xi
-xi+1)
2
-xi+2
Step 9. If 2τ ≤ |κ|is true, then embed ν in xi+2 via the LSB technique. Return to Step 1.
Step 10. If the conditions of ν = α and 0 ≤ (
(xi
-xi+1)
2
-xi+2) < τ are satisfied, then the bundle
“carried” α and go to Step 1, otherwise, if ν = α, then repeat adjust the coefficients (xi, xi+1) and
xi+2 either by increment or decrement, respectively, until 0 ≤ (
(xi
-xi+1)
2
-xi+2) < τ is true and
proceed to Step 1.
Step 11. If the conditions of ν = β and -τ ≤ (
(xi
-xi+1)
2
-xi+2) < 0 are satisfied, then the bundle
“carried” α and go to Step 1, otherwise, if ν = β, then repeat adjust the coefficients (xi, xi+1) and
xi+2 either by increment or decrement, respectively, until -τ ≤ (
(xi
-xi+1)
2
-xi+2) < 0 is true and
proceed to Step 1.
Step 12. If the conditions of ν = γ and τ ≤ (
(xi
-xi+1)
2
-xi+2) < 2τ are satisfied, then the bundle
“carried” γ and go to Step 1, otherwise, if ν = γ, then repeat adjust the coefficients (xi, xi+1) and
xi+2 either by increment or decrement, respectively, until τ ≤ (
(xi
-xi+1)
2
-xi+2) < 2τ is true and
proceed to Step 1.
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Step 13. If the conditions of ν = δ and -2τ ≤ (
(xi
-xi+1)
2
-xi+2) < -τ are satisfied, then the bundle
“carried” δ and go to Step 1, otherwise, if ν = δ, then repeat adjust the coefficients (xi, xi+1) and
xi+2 either by increment or decrement, respectively, until-2τ ≤ (
(xi
-xi+1)
2
-xi+2) < -τ is true and
proceed to Step 1.
Step 14. Stop.
Bit Extraction
The procedure of bit extraction is much simpler than that of bit embedding for the proposed
ECAT with the LSB method. The major steps of bit extraction are summarized in the following
algorithm.
Algorithm 2: Extracting hidden message from marked ECG.
Input: Marked ECG Ĥand control integer τ.
Output: Extracted message W.
Method:
Step 1. Input a marked bundle {x̂i
}i=0
n-1
from Ĥ and compute the offset η = x̂i
-x̂i+1. If the end of
input is encountered, then go to Step 6.
[Phase I]
Step 2. If |η| < 2τ is true, then two hidden bits can be extracted from the bundle, otherwise
proceed to Step 3. That is, if 0 ≤ (x̂i
-x̂i+1) < τ, then “00” are recognized, else if -τ ≤ (x̂i
-x̂i+1) <
0, then “01” is identified, else if τ ≤ (x̂i
-x̂i+1) < 2τ, then “10” are recognized, otherwise, “11” is
identified.
Proceed to Step 4.
Step 3. Four hidden bits can be identified from the coefficients x̂i and x̂i+1 via the LSB.
[Phase II]
Step 4. Compute the offset κ =
x̂i
-x̂i+1
2
-x̂i+2 If the condition of |κ| ⋖ 2τ is satisfied, then two 2
hidden bits can be extracted from the bundle, otherwise proceed to Step 5. Namely, data bits
“00,” “01,” “10,” and “11,” can be recognized if one of the following conditions:
0 ≤ (
x̂i
-x̂i+1
2
-x̂i+2) < τ,-τ ≤ (
x̂i
-x̂i+1
2
-x̂i+2) < 0, τ ≤ (
x̂i
-x̂i+1
2
-x̂i+2) < 2τ, and -2τ ≤ (
x̂i
-x̂i+1
2
-x̂i+2) <
-τ is satisfied, respectively. Return to Step 1.
Step 5. Two hidden bits can be identified from the coefficient x̂i+2 via the LSB. Proceed to Step
1.
Step 6. Assemble all of the extracted bits and form a watermark.
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
Step 7. Stop.
From the above Algorithms we can see that the control parameter plays an important role in
the proposed ECAT method. In fact, the SNR performance of the algorithm is gradually
decreased as increased. The reason is that the larger the value of , the larger the number of
bundles encoded by the ECAT, which may result in significant distortion from the marked
bundle. On the other hand, the payload size of the algorithm is increased as decreased. Due to
the less value of , the larger the number of bundles encoded by LSB, which provides the larger
hiding space.
EXPERIMENTAL RESULTS
A total of 23-set of ECG hosts were tested in our simulations. These test data were obtained
from the MIT-BIH Arrhythmia Database [14]. Each ECG host was composed of 30,000 samples.
Two commonly used objective measures such as signal-to-noise ratio (SNR) and percentage
residual difference (PRD) are used to evaluate performance. Both are defined as follows:
SNR = 10 log10
∑ si
2
i
∑ (si
-ŝi
)
2
i
(3)
and
PRD = √
∑ (si
-ŝi
)
2
i
∑ si
2
i
100% (4)
where si
, and ŝistand for the coefficients in the original ECG, marked ECG, respectively. First,
the payload/SNR/PRD performance of traditional CAT [13] is compared with that of our ECAT.
Table 1 indicated that the average payload of our method is 1.8 times larger than that of
traditional CAT [13] with the competitive SNR and PRD values. It can be summarized from
Table 1 that the ECAT (using = 3) do provide larger hiding storage than traditional CAT (using
= 20) does. Subsequently, trade-off between SNR and payload for the proposed ECAT with the
LSB method using various (from 2 to 9) is depicted in Fig. 1.
At first glance, it seems that the performance cures in the figure against common sense. (Since
the larger the payload size, the less the SNR value.) As mentioned in Section 2.2, the less
employed by the proposed method, the less number of the host blocks encoded by ECAT, and
the larger number of the blocks encoded by LSB. Namely, it resulted in less distortion
introduced from the procedure of the coefficient adjustments, but increased hiding storage by
the LSB substitution. From the figure we can see that ECG106 has the best performance in
payload and SNR among the test ECGs, followed by ECG104, and ECG100 is the worst. In
addition, relationship between PRD and payload of our method is depicted in Fig. 2. The figure
confirmed the fact that the larger the SNR, the less the PRD, and vice versa. As we know, the
larger the SNR, the smaller the PRD, and the better performance obtained by an ECG
steganography.
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Table 1. Performance comparison between traditional CAT and our ECAT.
ECG Payload/SNR/PRD
hosts Traditional CAT†
[13] Our ECAT
100 19,135/ 54.68/ 0.184 36,822 / 52.03/ 0.250
101 19,142/ 53.17/ 0.220 35,362 / 51.80/ 0.257
102 19,452/ 53.07/ 0.222 36,160/ 51.71/ 0.260
103 18,975/ 53.33/ 0.190 35,396 / 51.81/ 0.257
104 18,931/ 51.55/ 0.264 33,024 / 52.03/ 0.250
105 18,591/ 52.21/ 0.245 33,174 / 51.86/ 0.255
106 19,125/ 52.17/ 0.246 33,692 / 51.60/ 0.263
108 19,786/ 51.37/ 0.270 34,198/ 51.62/ 0.262
109 18,107/ 51.70/ 0.260 31,788/ 51.83/ 0.256
Average 19,027/ 52.58/ 0.233 34,402/ 51.81/ 0.257
†When the method used the size of the host block is 3.
Fig. 1. Trade-off between SNR and payload of our method.
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
Fig. 2. Trade-off between PRD and payload of our method.
To further examine the hiding performance of the proposed method, the resulting payload and
SNR of the test ECGs with five different are illustrated in Fig. 3. Figure 3(a) shows that the
hiding capacity provided by ECG107 has the largest payload size, followed by ECG109, and ECG
108 provided the least payload as < 2. Figure 3(b) indicates that ECG 107 provided the best
SNR as < 4 and the least SNR as > 2. The distortions introduced by the proposed method
using = 6 (in the first 5-s interval) from the marked ECGs are illustrated in Fig. 4. The marked
signal introduced by our method (red line) was approximately similar to the original one (blue
line).
(a)
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(b)
Fig. 3. Hiding performance of our method using five different . (a) payload and (b) SNR.
(a)
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
(b)
(c)
(d)
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(e)
(f)
Fig. 4. Close observations of the marked ECGs introduced by our method using = 6. (a)
ECG100, (b) ECG101, (c) ECG102, (d) ECG111, (e) ECG112, and (f) ECG113.
Performance comparison of existed techniques and our method in several test ECGs is given in
Table 2. It is clear that the payload of our method is the largest among the compared methods.
Namely, the payload provided by our method is two times larger than that provided by Yang
and Wang [10], and is nearly two times bigger than that provided by Yang et al. [12]. In addition,
the hiding storage of the proposed method is 1.84 times larger than that of the Yang and Wang
[13]. In summary, the averages SNR of the proposed method is superior to that of other three
CAT-based schemes. Furthermore, another objective measure which often used to evaluate the
performance of data hiding is peak signalto-noise ratio (PSNR). The PSNR and payload
performance comparison of various methods is listed in Table 3. Obviously, the average
payload of our method is 6.13 times larger than that of the other two schemes [8, 11], while our
PSNR is still better than theirs. The PSNR is defined as follows:
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Ching-Yu, Y., Tsung-Hsiang, H., & Pei-Yun, C. (2023). A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least
Significant Bit Substitution. Transactions on Engineering and Computing Sciences, 11(3).122-134.
URL: http://dx.doi.org/10.14738/tecs.113.14919.
PSNR = 10 log10
Max(si
)
2
1
K
∑ (si
-ŝi
)
2
i
(5)
where Max(
s
i) and K stand for the maximum value of si and the number of input samples,
respectively. From Tables 2 and 3 we can conclude that an efficient ECG steganography is
achieved by the proposed method.
Table 2. SNR/payload performance comparison between various methods.
ECG data SNR/Payload
Yang and Wang
[10]
Yang et al.* [12] Yang and Wang†
[13]
Our method
100 48.19/ 19,604 50.96/ 21,349 50.18/ 24,146 50.36/ 41,156
101 47.39/ 19,610 48.17/ 21,911 49.07/ 24,427 50.00/ 41,356
102 51.60/ 19,602 49.74/ 21,702 50.04/ 23,542 50.04/ 41,222
103 44.92/ 19,600 49.53/ 21,171 49.93/ 21,473 49.89/ 41,459
104 46.84/ 19,602 47.61/ 21,265 49.95/ 21,312 50.17/ 42,304
111 49.43/ 19,630 47.49/ 21,638 50.28/ 21,651 49.83/ 41,894
200 47.36/ 19,608 47.53/ 20,808 50.40/ 20,474 50.01/ 42,780
201 50.13/ 19,612 45.94/ 21,153 50.40/ 23,160 50.51/ 41,506
202 50.16/ 19,632 49.01/ 21,407 50.46/ 23,599 49.69/ 41,096
232 51.58/ 19,620 47.67/ 21,846 49.08/ 22,542 49.57/ 41,128
Average 48.76/ 19,612 48.37/ 21,425 49.95/ 22,633 50.01/ 41,590
*The size of a host block used by the method is 4*4
† The size of the host blocks employed by the method is larger than 3 * 3.
*Our method used the control parameter = 4.
Table 3. PSNR/payload performance comparison of various methods.
Jero et al. [8] Pandey et al. [11] Our method
54.75/ 10,650 58.01/ 10,650 63.01/ 9,800
51.13/ 14,450 55.55/ 14,450 61.83/ 12,800
45.12/ 18,022 53.62/ 18,022 59.84/ 20,000
39.52/ 21,873 51.98/ 21,873 58.29/ 28,800
34.46/ 25,149 50.79/ 25,149 56.86/ 39,200
Average 45.00/ 18,029 Average 53.99/ 18,029 Average 59.97/ 110,600
CONCLUSION
In this article, we presented an efficient ECAT to promote the payload size of traditional CAT
scheme. The payload of the former is apparently larger than that of the latter, while the
resultant SNR (or perceived quality) of ECAT is competitive to that of CAT. In addition, a large
number of data bits can be embedded in an ECG host as the proposed ECAT with the LSB
method. Experimental results confirmed that both the average payload and SNR/PSNR of our
method are superior to those of the variants of the CAT and existed schemes. In addition, the
resultant perceived quality is good. Due to the algorithm is simple, the application of our
method can be found in portable (or wearable) biometric measurements.
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References
[1]. Eielinska, E., et al. Trends in steganography. Comm. of the ACM, 2014. 57(3): p. 86-95, doi:
10.1145/2566590.2566610.
[2]. Hussain, M. A., et al., Image steganography in spatial domain: A survey, Signal Processing: Image
Communication, 2018. 65(7): p. 46-66, DOI: 10.1016/j.image.2018.03.012.
[3]. Hsiao, C.Y., et al., Simple and robust watermarking scheme based on square-root-modulus technique,
Multimedia Tools and Applications, 2018. 77(5): p. 30419-30435, DOI: 10.1007/s11042-018-6121-3.
[4]. Liu, S., et al., Digital image watermarking method based on DCT and fractal encoding, IET Image Proc., 2017.
11(10): p. 815-821, DOI: 10.1049/iet-ipr.2016.0862.
[5]. Sahu, A.K. and Swain, G., Data hiding using adaptive LSB and PVD technique resisting PDH and RS analysis,
Int. Journal of Electronic Security and Digital Forensics, 2019. 11(6): p. 458-476, DOI:
10.1504/IJESDF.2019.102567.
[6]. Kadhim, I.J., et al., Comprehensive survey of image steganography: Techniques, Evaluations, and trends in
future research, Neurocomputing, 2019. 335(3): p. 299-326, DOI: 10.1016/j.neucom.2018.06.075.
[7]. Yang, C.Y. and Wang, W.F., Effective electrocardiogram steganography based on coefficient alignment,
Journal of Medical Sys., 2016. 40(12), DOI: 10.1007/s10916-015-0426-9.
[8]. Jero, S.E., et al., Imperceptability-robustness tradeoff studies for ECG steganography using continuous ant
colony optimization, Expert Systems with Applications, 2016. 49(1): p. 123-135, DOI:
10.1016/j.eswa.2015.12.010.
[9]. Shiu, H.J., et al., Preserving privacy of online digital physiological signals using blind and reversible
steganography, Computer Methods and Programs in Biomedicine, 2017. 151(11): p. 159-170, DOI:
10.1016/j.cmpb.2017.08.015.
[10]. C.Y. Yang and W.F. Wang, “An improved high-capacity ECG steganography with smart offset coefficients,”
The 14th Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2018),
Nov. 26-28, Sendai, Japan, 2018.
[11]. Pandey, A., et al., A novel fused coupled chaotic map based confidential data embedding-then-encryption of
electrocardiogram signal, Biocybernetics and Biomedical Engineering, 2019. 39(2): p. 282-300, DOI:
10.1016/j.bbe.2018.11.012.
[12]. Yang, C.Y., et al., Adaptive electrocardiogram steganography based on 2D approach with predetermined
rules, Asian Journal of Computer and Information Systems, 2020. 8(1): p. 1-10, DOI: 10.24203/ajcis.
v8i1.6059.
[13]. Yang, C.Y. and Wang, W.F., Progressive data hiding in integer wavelet transform of electrocardiogram by
using simple decision rule and coefficient calibration, Revue d'Intelligence Artificielle, 2020. 34(2): p. 1120,
DOI: 10.18280/ria.340102.
[14]. Moody, G.B. and Mark, R.G. The impact of the MIT-BIH arrhythmia database. IEEE Eng. in Medical and Biol.,
2001. 20(3): p. 45-50, DOI: 10.1109/51.932724.