SECURE MEDICAL DATA TRANSMISSION MODEL FOR IOT-
BASED HEALTHCARE SYSTEMS
Meruva Lahari Krishna1, D. Chiranjevulu2
1 PG Scholar, Dept of Electronics and Communication Engineering, Sri Sivani College of
Engineering, Srikakulam, Andhra Pradesh, India. 2 Associate Professor, Dept of Electronics and Communication Engineering, Sri Sivani College
of Engineering, Srikakulam, Andhra Pradesh, India.
Abstract— In this paper, a novel image watermarking method is proposed which is based on
discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value
decomposition (SVD). First, in the embedding process, the host image is decomposed into a
number of sub-bands through multilevel DWT, and the resulting coefficients of which are then
used as the input for HD. The watermark is operated on the SVD at the same time. The
watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization
algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling
factor through the proposed objective evaluation function. The proposed method is compared to
other research works under various spoof attacks, such as the filter, noise, JPEG compression,
JPEG2000 compression, and sharpening attacks. The experimental results show that the
proposed image watermarking method has a good trade-off between robustness and invisibility
even for the watermarks with multiple sizes.
Index Terms—chaotic S-block, reversible data hiding, Lossless data hiding, encryption,
cryptography, SSI, BSSI.
I. INTRODUCTION
The explosive growth of internet usage makes information dissemination become increasingly
easier than ever, leading to serious copyright infringement problems, such as unauthorized
copying [1], distribution [2], [3] and modification of digitized works [4], [5]. In order to improve
the effective utilization of the network information, the copyright protection is becoming
particularly important [1]. As one of the widely used protection techniques, watermarking
method has been applied in many fields of multimedia copyright protection [1]–[5].
Watermarking is a common information embedding technique to protect the image, video and
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audio information. It integrates the key information into the modalities by invisibly modifying
the data. Therefore, invisibility and robustness are two major metrics for evaluating the
effectiveness of the watermarking techniques [1]. Based on these two metrics, watermarking
techniques can be broadly classified into three groups, i.e. the robust, fragile and semifragile
watermarking [1]. The robust watermarking is crucial for the image data protection because it
does not significantly reduce the visual quality of the watermarked image, and can withstand
various attacks. It is therefore widely used for copyright protection and ownership verification.
Fragile watermarking is only used to ensure the completeness of the image rather than to verify
actual ownership [2]. Even though it can detect any unauthorized modification or any
modifications of the watermarked images, it also destroy the completeness of the watermark if
any change happens. Semi-fragile watermarking combines the advantages of fragile
watermarking and robust watermarking, with the aim to detect unauthorized manipulations while
keeping the robustness against authorized manipulations [3]. As for the robust watermarking
method, the watermark information is often directly embedded in the spatial domain, i.e. the
watermark data is embedded into the host image by modifying the pixels spatially [4]. This
operation is easy to implement but it is not robust enough against the geometric and image
processing attacks [5]. In the meantime, the embedding process can also be completed in the
transformed domains, e.g. the discrete cosine transform (DCT) [6]–[11], discrete Fourier
transform (DFT) [12]–[14], discrete wave transformation (DWT) [15]–[17], and singular value
decomposition (SVD)[18]–[20]. To ensure the robustness of the embedding algorithms,
frequency domain analysis is utilized to find out the possible locations of the embedding
watermark coefficients, Research shows that the human vision is more sensitive to low and
middle frequency coefficients. Therefore, a good performance of the operation methods in the
transformed domain can be achieved, especially when the watermarks are embedded within the
low frequency ranges. Moreover, it is reported that the DWT-based watermarking methods have
the advantages of multi-resolution, good energy compression, and an imperceptible visual
quality, thus it can be used for image watermarking. However, the DWT-based watermarking is
difficult to resist geometric attacks. This drawback can be addressed by extracting the geometric
features of the image by using the matrix decomposition. Therefore, the method based on the
DWT and matrix decomposition is widely used in the image watermarking to resist image
processing and geometric attacks. The most common matrix decompositions used in the
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watermarking include SVD and Hessenberg decomposition (HD). The SVD provides a general
and quantitative view on the image changes, and its structural information is crucial in predicting
the image quality. Note that the singular vectors can represent structural information.
Modifications in singular vectors are linked to the singular values which primarily represent the
image luminance. Based on this, some robust watermarking methods are introduced in, which are
based on DWT and SVD. The major concern of the SVD-based watermarking methods is the
false positive problem, which can be solved using the encryption operation. Specifically, the
components of SVD are encrypted by the chaotic systems, which can guarantee the
watermarking method having a strong security performance, i.e. the false positive problem is
solved. The matrix decomposition of HD has also been widely used for watermarking. It
provides a method to embed the watermarks. However, the aforementioned watermarking
methods require that the size of watermark is fixed. The watermarking methods under various
sizes of watermarks are still required to be investigated. In addition, the performances of
invisibility and robustness are two vital metrics for the image watermarking, and the trade-off
between them is always a challenging. Recently, several bio-inspired algorithms are used to
address this problem, such as differential evolution artificial bee colony (ABC) firefly algorithm,
particle swarm optimization (PSO) and fruit fly optimization algorithm (FOA). In the approaches
of, the FOA is used to solve the trade-off problem, and the performances are improved. In this
paper, FOA is employed to optimize the parameters of the proposed algorithm and a trade-off
between the invisibility and robustness is achieved. Based on these discussions, a novel image
watermarking algorithm which combines DWT, HD and SVD is proposed in this paper. The
performance test shows that this method has good invisibility and robustness, and does not have
the constraint of fixed sizes of watermarks. Specifically, this work exploits an objective
evaluation function (OEF) and FOA to find an adaptive and optimal scaling factor to achieve a
trade-off between invisibility and robustness. The main contributions of this work include: (1)
The proposed watermarking method satisfies the multiple sizes of watermarks, and the tradeoff
between invisibility and robustness is achieved with a good performance; (2) OEF is proposed to
assist in finding the optimal scaling factor to solve the contradiction between the invisibility and
robustness, and FOA is employed to find the optimal factor; (3) HD is used to change the
coefficients before SVD operation which can enhance the robustness of the watermarking; (4)
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Results show that the proposed watermarking method is robust under the attacks of filter, noise,
JPEG compression, JPEG2000 compression and sharpening.
II. LITERATURE SURVEY
The appropriate background of literature and the concept of digital image watermarking are
reviewed in this chapter. The copyright protection of multimedia content has become a critical
issue now days due to easy copying, the latest developments in digital transmission and
widespread of broadband networks and the internet. The transmission of information takes place
in different forms and is used in many applications, where the communication must be done in
secret form. Such secret communication techniques include the transfer of medical data, bank
transfers, corporate communications, purchasing using bank cards, a large amount of information
through emails and etc. Steganography, cryptography and watermarking are the different
techniques used to perform secret communication. Watermarks can be embedded within images
by modifying these values, i.e. the transform domain coefficients. In case of spatial domain,
simple watermarks could be embedded in the images by modifying the pixel values or the Least
Significant Bit (LSB) values. However, more robust watermarks could be embedded in the
transform domain of images by modifying the transform domain coefficients. In 1997 Cox et al.
presented a paper ―Secure Spread Spectrum Watermarking for Multimedia‖ , one of the most
cited paper (cited 2985 times till April‘ 2008 as per Google Scholar search), and after that most
of the research work is based on this work. Even though spatial domain based techniques cannot
sustain most of the common attacks like compression, high pass or low pass filtering etc.,
researchers present spatial domain based schemes [1].
III. THE PROPOSED WATERMARKING SCHEME
This paper proposes a healthcare security model for securing a medical data transmission in IoT
environments. The proposed model composes of four continuous processes: (1) The confidential
patient's data is encrypted using a proposed hybrid encryption scheme that is developed from
both AES and RSA encryption algorithms. (2) The encrypted data is being concealed in a cover
image using either 2D-DWT-1L or 2D-DWT-2L and produces a stego-image. (3) The embedded
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data is extracted. (4) The extracted data is decrypted to retrieve the original data. Fig. 1 shows
the general framework of our proposed model for securing the medical data transmission at both
the source's and the destination's sides.
Figure 1. Diagram of the watermark embedding process
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In this process, A Haar-DWT was implemented. Throughout Haar-DWT, 2D-DWT-2L can be
formulated as a consecutive transformation using low-pass and high-pass filters along the rows
of the image; then the result decomposed along the columns of the image [21]. Fig. 2 elucidates
this process. Fig. 2 illustrates the elemental decomposition process for C୨ ሺn, mሺ image of a
size N ሺ M in four decomposed subband images which are referred to a high-high (HH), a high-
low (HL), a low-high (LH), and a low-low (LL) frequency bands. Fig. 3 shows the effect of the
decomposition process on the image.
Fig. 2. The decomposition process of DWT-2L
The proposed model implements the steganographic scheme. The steganographic scheme is
composed of embedding and extraction processes. The embedding process takes a cover image C
and a secret text message T as input and generates a stego-image S. While the extraction process
inversely extracts the embedded message. It can be mathematically modeled as given in the
following equations below. Throughout the embedding process, the secret text is transformed
into an ASCII format and then divided into even and odd values. The odd values are concealed in
vertical coefficients mentioned by LH2. The even values are concealed in diagonal coefficients
specified by HH2. The algorithm that is used in the embedding procedure by evolved 2D-DWT-
2L is described below.
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After incorporating the text into the cover image, the 2DDWT-2L technique is carried to extract
the secret message and retrieve the cover image. The extraction algorithm is described below.
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Once the secret text message has been extracted, the cover image is synthesized from the
reconstructed approximation by calling the idwt2 for the second level and then for the first level
Decryption refers to the process of converting the encrypted data back to the user in a well-
known format; which is the reverse of the encryption process. The same key used by the sender
has to be used over the cipher-text throughout the encryption process. The decryption process
can be mathematically expressed as given in the following equations below.
FIGURE 3. new Procedure of the watermarking embedding.
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FIGURE 4. new Procedure of the watermarking extraction.
The host image C and the watermark W are the input in the watermarking embedding algorithm,
and the output is watermarked host image C ∗ . The sizes of C, W, C ∗ are M × M, N × N and M
× M, respectively. In addition, this watermarking method can accommodate watermarks with
multiple sizes, and the host image is decomposed by R-level DWT. The procedure of the
watermarking embedding is shown in Fig. 1 and the detailed embedding steps are specified in the
following steps.
• Step 1. Based on R-level DWT, C is decomposed into the components of LL, LH, HL, HH,
where R=log2 M N .
• Step 2. HD is performed on LL, and it is shown as PHPT = HD(LL). (9)
• Step 3. Apply SVD to H HUwHSwHVT w = SVD(H). (10)
• Step 4. W is applied with SVD, i.e. UwSwV T w = SVD(W). (11) Then the operation of Uw, V
T w is encrypted by the chaotic system which is generated by the Logistic map. This specified
encryption is detailed in the experimental analysis of false positive problem. The encrypted two
components are marked as Uw1 and V T w1 .
• Step 5. Compute an embedded singular value HS∗ w by adding HSw and Sw with a scaling
factor α by HS∗ w = HSw + αSw. (12)
Step 6. The watermarked sub-band H ∗ is generated by using the inverse SVD, i.e. H ∗ =
HUwHS∗ wHVT w . (13)
• Step 7. A new low-frequency approximate sub-band LL∗ is reconstructed based on the inverse
HD which is given by LL∗ = PH∗P T . (14)
• Step 8. The watermarked image C ∗ is obtained by performing the inverse R-level DWT.
In the watermarking extraction algorithm, the input is the watermarked host image C ∗ , and the
output is extracted watermark W∗ . The size of W∗ is N×N. The procedure of the watermarking
extraction is shown in Fig. 2 and the detailed extracting steps are shown as follows
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• Step 1. The watermarked host image C ∗ is decomposed into four sub-bands by R-level DWT,
which include LLw, LHw, HLw, HHw.
• Step 2. HD is performed on LLw by PwHwP T w = HD(LLw
• Step 3. Apply SVD to Hw, i.e. HU∗ wHSb∗ wHV∗ w T = SVD(Hw).
• Step 4. The extracted singular value S ∗ w is gained by S ∗ w = (HSb∗ w − HS∗ w )/α.
• Step 5. The components Uw1 and V T w1 are decrypted by the chaotic system. Then the
decrypted two components are marked as Uw2 and V T w2 . The extracted watermark W∗ is
reconstructed by inverse SVD, which is described by W∗ = Uw2S ∗ wV T w2 .
IV. EXPERIMENTAL RESULTS
Figure 3. watermark processed images
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Figure 4. watermark extraction image.
V. CONCLUSION
In this paper, a novel image watermarking method based on DWT-HD-SVD transforms is
proposed. Specifically, the FOA is used to find the optimal scaling factor. The invisibility and
robustness of this method are analyzed by the numerical simulation experiments and the results
show the watermarked host images have good visual quality, PSNRs, and SSIMs. Besides, the
watermarks can be clearly extracted from the watermarked host image under different attacks
with the relatively high NCs. Moreover, even for the watermarks with different sizes, the
proposed image watermarking method can achieve a good invisibility and robustness. In
addition, the comparison with the related works are listed and the corresponding metric values
show that the proposed method has a better performance in terms of robustness for most attacks.
It is worth noting that the proposed method is highly robust to defend the filter, noise, JPEG
compression, JPEG2000 compression and sharpening attack. In the future work, the proposed
watermarking method may be need to pay attention on resisting more attack, such as rotation
attack and cropping attack.
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