EPNN Based High Secure Intensive Hidden Digital Watermark Application in Telemedicine

In modern health environment, many clinicians or health care providers share medical multimedia content like images and electronic patient data as a part of applications of telemedicine such as telediagnosis, teleradiology, telesurgery, teleconsulting, etc. With the increased use of teleradiology, the need for security and privacy concern issues in telemedicine is also growing. Another issue is that the transmitted multimedia content is very much tamper prone. So there is a desperate need for implementation of secure medical transfer schemes like medical image watermarking to achieve the security services of cryptography like authentication, integrity, and confidentiality. Authentication verifies that whether the received image arrives from the correct source and belongs to the intended patient or not, confidentiality ensures that only legitimate users have access to the transmitted image and whereas integrity checks that whether the received image has not been transformed by the unauthorized users [1].

Medical Image watermarking can be used to serve the purpose of transferring the medical history of a patient through the web. Patient details can be embedded in the medical images in an encrypted form. At the receiver’s end, it can be extracted and decrypted to verify the patient details. Through medical image watermarking patient information is maintained confidentially. The proposed approach also provides authenticity and integrity for the content of the medical image.

The proposed methodology relies on Lifting Wavelet Transform. LWT coefficients are represented by a finite precision number which helps for lossless coding. So LWT provides lossless compression.LWT can reconstruct the image without any loss as LWT coefficients are integers values that can be accumulated without any round-off errors. LWT implements the lifting scheme. Lifting the wavelet scheme is treated as the best solution than DWT as it is more advantageous for different applications [2]. It is the second generation of the fast wavelet transform. As lifting wavelet increases smoothness and reduces aliasing effect it is considered as better for the reconstruction of the image when compared with general wavelet transform [3]. Applying LWT improves the robustness of the watermark as it enhances the integrality of an embedded watermark in the host image, which results in reduced information loss.

The performance of traditional watermarking methods can be enhanced by artificial neural networks [4]. The relation of watermarked image and watermark image can be memorized by artificial neural networks. One of the artificial neural network methods used is the Probabilistic Neural Network (PNN). This consists of nodes of 4 layers [4]. They are input, pattern, summation and output layers [4]. PNN identifies a learning category and finds out the possibility of similar input sample with the help of the radial bias function.

The proposed approach uses extended Euclid's algorithm to get modular multiplicative inverses so that they can be replaced by cover image pixel values. Modular multiplicative inverse exists only if two integers are coprime i.e. when their gcd is 1. In this algorithm, every divisor and remainder is represented as a linear combination of the two numbers. This algorithm finds x such that ax≡(1modm) [5]. PNN is also used in this approach to improve the robustness of the watermarked image.

The proposed work is organized as follows; Related Work is analyzed in SectionII Section III analyzes the proposed work in detail. Experimental results were shown in section IV.

3.1 Encryption approach

Input: Patient Identification Data, the Patient Fingerprint image

Output: Ciphertext

1. Read Plaintext P.
2. Read key matrix K ={K| K( i, j ) is a printable character on the  keyboard } according to ULDS where U={ u | u is a uppercase letter} , L={ l |  l is a lower case letter}, D={ d| d is a digit}, S= { s | s is a special symbol}
3. Encode each plaintext character to get Ciphertext T using a random variable from Ri, i ∈ {1, 2, …, N}, where N is the length of K.
4. Embed the ciphertext into watermark Iw using the LSB and penultimate LSB. The encryption and embedding approach is shown in Figure 1.

1.png

Figure 1. Encryption and embedding

3.2 Watermark embedding approach

Watermark image: Patient fingerprint image in which ciphertext is embedded

Cover Image: Patient Medical Image

Output: Patient Watermarked medical Image.

1. Apply the second level LWT on Cover Image(I_c).

2. Let Part (I_w) be the set of partitions of  I_w.

3. ( X_p),   I_w ∈ Part (I_w), 1 < p ≤ 64 and each | X_p |  is 16-bit partition.

4. A1_w= Embed(X_p , A1) , 1< p< 16 using

         Extended Euclid’s Approach

         Loop Begin:

         Let  M = X_p (i , j) , N=A1(i , j) ,where 1< i ≤ 16,1< j ≤ 16

         if gcd (M , N)=1

         Find T such that M *T ≡ 1 mod 255 then

         N=M       

         else

         N=M

         End

         A1_w(i, j)=N

         Loop End

5. Repeat the step4 for H1 to get H1_w, where 17 <  p ≤ 32.

6. Repeat the step4 for D1 to get D1_w , where 33 <  p ≤ 48.

7. Repeat the step4 for V1 to get V1_w, where 49 <  p ≤64.

8. I_wk= ilwt2 (A1w, H1w, D1w, V1_w ).

9. I_wk =PNN(I_wk, I_c).

3.3 Extraction approach

1. Read Watermarked Image

2. [A1 _w, H1 _w, D1 _w, V1 _w]=lwt2 ( I_wk )

3. X_pA= Extract  (X_pw, A1_w) , 1< pw< 16 using  Extended Euclid’s Approach

1. Let Part (A1_w ) be the set of partitions of A1w

2. ( X_pw )  ∈ A1_w ∈ Part (A1_w ), 1 < pw ≤ 16 and each  | X_pw |  is a 16-bit partition.

3.  Loop Begin:

           Let M = X_pw (i , j)where 1< i ≤ 16, 1< j ≤ 16​

           if M*T ≡ 1 mod 255, then

           T=N

           eles

           N=M

           End

            A1’(i, j)=N

            Loop End

4. Repeat the step3 for H1_w to get X_pH, where 17 <  p ≤ 32.

5. Repeat the step3 for D1_w to get X_pD, where 33 <  p ≤ 48.

6. Repeat the step3 for V1_w to get X_pV, where 49 <  p ≤64.

7. Combine the partitions X_pA X_pH, X_pD, X_pV to get extracted watermark I_Ewk.

8. Extract the ciphertext T from watermark using the inverse process of encryption approach. The process is shown in Figure 2.

2.png

Figure 2. Extraction approach

A robust and secure digital watermarking algorithm based on extended Euclid’s and PNN approach in the wavelet domain is proposed in this paper. This study exhibits significantly anonymity and robustness of the watermark. LWT based 2-level decomposition is performed on the original image to get LL2, LH2, HL2, HH2 wavelets. Watermark image of size 64*64  and  2- level LWT wavelets are divided into 16*16 blocks. Using the extended Euclid's approach watermark image is embedded into the original image. The watermarked image is optimized using PNN approach. The proposed algorithm shows better invisibility of the watermark and also provides extraction of the watermark from the watermarked image. The strength of the proposed algorithmic approach is analyzed against various image attacks like JPEG compression, Gaussian noise, salt and pepper noise, and median filtering, Also the approach has shown better Normalized Correlation when compared with the existing approach with respect to mentioned attacks.