SECURE MEDICAL DATA TRANSMISSION MODEL FOR IOT- … · discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding

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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