Multimedia Content Protection through Reversible ......Multimedia Content Protection through...

Multimedia Content Protection

through Reversible Watermarking:

Theory and Implementation

Ruchira NaskarRajat Subhra Chakraborty

Department of Computer Science and Engineering

Indian Institute of Technology, Kharagpur

Email:{rschakraborty,ruchira}@cse.iitkgp.ernet.in

8th International Conference on Information Systems Security

December 15-19, 2012

Ruchira NaskarPh.D. Student

Rajat Subhra ChakrabortyAssistant Professor

Contents� Digital Watermarking

� Reversible Watermarking� Overview

� Applications

� Motivation behind Research� Motivation behind Research� A Case Study

� State-of-the-Art� Five Broad Classes of Reversible Watermarking Algorithms

� Challenges Involved� Embedding Retrieval Information

� Minimizing FRR of Cover Image Pixels

� Probable Solutions2


Contents� Implementation Issues

� Efficient Implementation: Parallel Processing

� Efficient Implementation: on FPGA

� Evaluation of Reversible Watermarking Algorithms� Software Evaluation� Software Evaluation

� Theoretic Evaluation

� Conclusion

� Bibliography

3


Digital Watermarking

Cover Data

Watermarked

Content to be protected

4


Embedder

Watermark

WatermarkedData Extractor

Extracted Watermark

Information about Cover Data

Digital WatermarkingPerceptible Watermarking

5


Imperceptible Watermarking

Reversible WatermarkingBit-by-bit reversal

6


Reversible Watermarking:

Applications

� 100% Cover Data Recovery

� Needed in highly security sensitive industries such as:

� Military

� Medical

Legal

7


� Legal

� Authentication of Digital Data

� Overwhelming majority of algorithms proposed are for grayscale images

Motivation behind Research in

Reversible WatermarkingReversible Watermarking

8


• Digital watermarking in the medical industry provides content

protection and integrity preservation of medical data, e.g. patient

images electronically transmitted over insecure channels in

telemedicine

• Electronic Patient Records (EPRs) kept embedded in form of

A Case Study from the Medical

Industry

9


• Electronic Patient Records (EPRs) kept embedded in form of

watermark, into medical images

- Useful for doctors, patients, clinical researchers,

insurance companies, hospitals

• Often responsible for information-loss:

- Cover data distortion caused due to watermark embedding

- Cannot be removed by general watermarking

- Highly undesirable in the medical industry

Aims:

• To investigate the effects of DRM, specifically digital

watermarking, on medical imaging applications

•

A Case Study from the Medical

Industry

10


• To investigate the need of 100% reversibility provided by

reversible watermarking

Automated Diagnosis of Malaria

• Malaria infection caused by Plasmodium vivax is a leading

cause of death world-wide

• Existing manual methods of malaria diagnosis, are tedious

and error-prone

11


• Hence the need for computer-aided automatic diagnostic

systems

Methodology

• Blood smears of 250

patients collected from

Midnapore Medical

College and Hospital and

Medipath Laboratory, W.B.Lossy

Technique

Reversible

Technique

12


Medipath Laboratory, W.B.

• According to doctors’

suggestions 50 best

prepared blood smears

were selected as our test

images

e e

e

Malarial Prediction Model*

• Total 26 features were extracted to distinguish the healthy and

malaria infected erythrocytes

13


malaria infected erythrocytes

• Those features include

- Geometric features, e.g. area, parameter, circularity

- Haralick Textural features, e.g. entropy, correlation, dissimilarity

• The Multivariate Logistic Regression Model was used for prediction1

* D. Das, M. Ghosh, C. Chakraborty, A.K. Maiti, M. Pal, “Probabilistic Prediction of Malaria using Morphological and Textural Information”, Proceedings of International Conference on

Image Information Processing (ICIIP) 2011, Nov. 2011.

Results

Original Test Images

Test Images containingResidual Distortions

14


AlgorithmResidual Distortion

(PSNR)Embedded bpp

LSB-substitution

Entire Image

G Component

Entire Image

GComponent

51.1421 55.9077 3.00 1.00

Distortions

Residual Image Distortion (after Watermark Extraction) and Embedded Watermark Size *

*Results averaged over 50 test images

ResultsClass Conditional Density Plots

15


(a) Original Test Images (b) Distorted Test Images


ResultsClass Conditional Density Plots

16




Results

• Overall Prediction Accuracy was measured as:

where Phealthy = number of predicted healthy erythrocytes

Prediction Accuracy

17


where Phealthy = number of predicted healthy erythrocytes

Nhealthy = number of erythrocytes actually healthy

Pinfected = number of predicted infected erythrocytes

Ninfected = number of erythrocytes actually infected

ResultsPrediction Statistics for Original Test Images

ActualPrediction Accuracy (%)

Infected Healthy

PredictedInfected 79 11 87.78

Healthy 8 178 95.70

Overall Prediction Accuracy (%) 91.74

18


Prediction Statistics for Test Images containing Residual Distortion after Lossy Watermarking Extraction

Actual Prediction Accuracy (%)Infected Healthy


Healthy 12 174 93.54


Inferences

• The accuracy of prediction drops considerably due to residual

distortion caused by lossy watermarking

• Reversible watermarking can restore the watermarked images to

their original forms without any distortion

• The accuracy of prediction performed on the restored images, is

as high as what is achieved from the original images

19


Prediction Statistics for Test Images Restored by Reversible Watermarking

Actual Prediction Accuracy (%)Infected Healthy


Healthy 8 178 95.70


Typical Classes of Reversible

Watermarking AlgorithmsWatermarking Algorithms

20


1. Integer Transform

Cover Image

Average (l), Difference (h)

Embed{0,1}b∈Watermark bit b 2h h' +=

Transform adjacent pixels (x, y)

yx h ;2yx l −=+=

21


Watermarked pixels (x’, y’)

Inverse Transform (l, h’)

−=++=2h' l y';

21h' l x'

Watermarked Image

J. Tian, “Reversible data embedding using a difference expansion”, IEEE Transactions on Circuits

Systems and Video Technology, 2003

2. Data CompressionL-level quantize each pixel x:

QL(x) = L ;

RL(x) = x - QL(x)Cover Image

WatermarkW

Quantized ValuesQL(x)

RemaindersRL(x)

LosslessCompression

Concatenate

L

x

22


W

Bit Stream H

Convert to L-ary Symbols

L-ary Symbols{0, 1, …, L-1}

+

∈Watermarked

Image

M.U. Celik, G. Sharma, A.M. Tekalp and E. Saber, “Lossless generalized-LSB data embedding”,

IEEE Transactions on Image Processing, 2005

3. Histogram Bin Shifting

…

0 1 … 254

Peak

…

Right-Shifted

0 1 … 255

Frequency

Frequency

CoverImage

23


…

0 1 … 255

‘0’‘1’

WatermarkedImage

Frequency

Z. Ni, Y.Q. Shi, N. Ansari and W. Su, “Reversible data hiding”, IEEE Transactions on Circuits and

Systems for Video Technology, 2006

4. Pixel PredictionPredict pixel x

x‘ =Predict (x, Neighboring pixels of x)

Cover Image

Predicted Pixel x ’

Compute Prediction Error

e = x – x‘

Original Pixel x

24


+

WatermarkedImage

e = x – x‘

Embed

{0,1}b∈Watermark bit

b)|e|(2sign(e)e' +××=

L. Luo, Z. Chen, M. Chen, X. Zeng and Z. Xiong, “Reversible image watermarking using

interpolation technique”, IEEE Transactions on Information Forensics and Security, 20108th International Conference on Information Systems Security

Cover Image

IDCT

xx x

x xx xx

x xxxxxxxxxxxxxxxx

Left Shift&

Embed bits

InverseIDCT

8

8

8

8

Divide into 8X8 blocks

5. Invertible Integer DCT

25


B. Yang, M. Schmucker, W. Funk, C. Busch and S. Sun, “Integer DCT based reversible

watermarking technique for images using companding technique”, Proceedings of SPIE, 2004.

Watermarked 8X8 block

xxxxxxxxxxxx

x x xx

Select IDCTcoefficients

Embed bits

Watermark

IDCT

WatermarkedImage

8

Challenges InvolvedChallenges Involved

26


Challenge 1: Embedding

Retrieval Information� Primary requirement of reversible watermarking: To restore the

cover data back to its original form bit-by-bit

� Additional retrieval information needed for bit-by-bit cover data

retrieval

� Retrieval information and the watermark together form the payload

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� Retrieval information and the watermark together form the payload

to be embedded

� Example of retrieval information:

� Location Map: A bit string to distinguish between cover data positions with

and without having watermark bits embedded

� Used widely in several state-of-the-art reversible watermarking algorithms

� Challenge: To reduce the size of this retrieval information

overhead, therefore to improve pure watermark embedding capacity

Use of Location Map: An Example

Predict pixel xx ’ = Predict (x, Neighboring pixels of x)

Cover Image


Compute Prediction Errore = x – x ’

Original Pixel x

A Prediction based Reversible Watermark Embedding Algorithm

28


+

WatermarkedImage

e = x – x ’

Embed

{0,1}b∈

b)|e|(2|'e| +×=

YesNo

|e|<threshold ?

Watermark bit

Use of Location Map: An ExampleA Prediction based Reversible Watermark Extraction Algorithm

Predict pixel xx ’ = Predict (x, Neighboring pixels of x)


WatermarkedImage

Compute Prediction Errore = x – x ’

Watermarked Pixel x

29


+

e = x – x ’

Extract

b)/2|e(||'e||,2)emod(| b

−==

YesNo

threshold2-1|e| >

Restored Image

Use of Location Map: An ExampleOverhead Bits

• Error Threshold (k):

o Only those prediction errors with absolute values <k are used for watermark embedding

o Controls embedding capacity

• Location Map:

o Used to identify pixels capable of causing under/overflow, i.e.(p < 0) or (p > 255), where p is a watermarked pixel


(pwm< 0) or (pwm> 255), where pwm is a watermarked pixel

o During extraction each watermarked pixel is tested for under/overflow.

o A pixel found capable of causing under/overflow during extraction, indicates one of the two possibilities:

o It was found to be capable of causing under/overflow during embedding, and hence was not used for embedding.

o Previously, it did not cause an under/overflow, but after embedding it has lost its embedding capability.

Use of Location Map: An ExampleWeighted Median based Prediction (R. Naskar and R.S. Chakraborty, IET Image Processing, 2012)

I. Select Base Pixels

II. Predict three sets of pixels consecutively, assigning appropriate

weights to the neighbors

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3Base Pixel


0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

Base Pixel

First Set Pixel

Third Set Pixel

Second Set Pixel


Weighted Median based Prediction

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

W=1

Prediction Formula for First Set of Pixels:

ξ(p(i,j)) = WM({p(i -1, j-1), p(i -1, j+1), p(i+1, j -1), p(i+1, j+1)}, {1, 1, 1, 1})

W=1


2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

Pixel to be predicted

Neighbor with assigned

weight ww

Prediction of a First Set Pixel

W=1W=1


Weighted Median based Prediction

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

Prediction Formula for Second Set of Pixels:

ξ(p(i,j)) = WM({p(i, j-1), p(i -1, j), p(i, j +1), p(i+1, j)}, {1, 2, 1, 2})

Prediction Formula for Third Set of Pixels:

ξ(p(i,j)) = WM({p(i, j-1), p(i -1, j), p(i, j +1), p(i+1, j)}, {2, 1, 2, 1}) W=2

W=1 W=1


0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

0 3 0 3 0 3 0 3 0 3

2 1 2 1 2 1 2 1 2 1

Prediction of a Second Set Pixel

W=2

W=1

W=1

W=2 W=2

Use of Location Map: An ExampleResults for Weighted Median based Prediction:

Embedding Capacity, Distortion, Location Map for Error Threshold

0 ≤ k ≤ 10 (Non-Zero Location Map for Higher k values)



Results for Weighted Median based Prediction:

Embedding Capacity, Distortion, Location Map for Error Threshold

0 ≤ k ≤ 10 (Zero Location Map for all 0 ≤ k ≤ 10 )


Challenge 2: Minimizing FRR of

Cover Image Pixels� In traditional reversible watermarking algorithms for digital images,

the watermark is a secure hash of the cover image (Eg. MD5, SHA)

� To authenticate the cover image at the receiver end:

� The extracted watermark is matched with the hash of the cover image

� If there is a match, cover image is accepted

36


� If there is a match, cover image is accepted

� A hash mismatch indicates tampering of the cover image while

transmission

� If there is a hash mismatch the entire cover image is rejected

� This may occur even due to a single bit mismatch

� Number of cover image pixels falsely rejected is extremely high in such cases

� Challenge: To minimize the False Rejection Rate (FRR) of the

cover image pixels in case of authentication failure

Challenge 2: Minimizing FRR of

Cover Image Pixels

ReversiblyWatermarked

Image Extractor

Restored Cover Image Compute Hash

1 0 1 1 0 … 1

37


Image Extractor

Extracted Watermark

Hash

Match

Yes No

Accept Cover Image

Reject Entire Cover Image

Cover Image Tampering and

False Rejection� Reversibly watermarked image rejection may be brought about by

� Intentional Tampering: Trivial for a man-in-the-middle adversary, reversible

watermarking being a fragile watermarking technique

� Unintentional Modification: For example, transmission through noisy

communication channel may modify one or more pixels of the cover image

� Unintentional Modifications are often found in military communications, and

38


� Unintentional Modifications are often found in military communications, and

are bound to occur each time the image is transmitted

� The convention is to reject the entire watermarked image at the

receiver end if it fails authentication, since there is no way to detect

the exact location(s) of tampering

� Repeated rejection requires re-transmissions of the entire

watermarked image

� This feature may be exploited by an adversary to bring about a form

of “DoS” attack

Probable Solutions� Tamper Localization Mechanism: Localizing the area(s) of

tampering and selective rejection of tampered cover image area(s) in

case of authentication failure

� Reject only the tampered area(s) instead of the entire image

� Re-transmission of only the tampered region(s) of the image,

39


� Re-transmission of only the tampered region(s) of the image,

instead of the entire watermarked image

� Also minimizes bandwidth requirement of the communication

channel

Probable Solutions� Determining the Region(s) of Interest within an image

� In various application domains of reversible watermarking, only some areas of

the image carry the bulk of the important information and are of interest to

the recipient

� If the tampering falls outside the region(s) of interest, the cover image is

accepted by the receiver, thus avoiding unnecessary rejection

40


accepted by the receiver, thus avoiding unnecessary rejection

� Otherwise the user can request re-transmission of only the tampered parts of

the image

Implementation IssuesImplementation Issues

41


Implementation Issues� Existing reversible watermarking algorithms involve various

complex mathematical operations for achieving reversibility property

� Invertible functions such as invertible integer transform, invertible IDCT

etc.

� Computation of Overhead Data such as cover image retrieval information

(location map), peak of pixel frequency histogram, thresholds etc.

42


(location map), peak of pixel frequency histogram, thresholds etc.

� Lossless Compression Techniques such as JBIG, Run Length Encoding,

LZW encoding etc.

� Such operations are absent in the implementation of their non-reversible

counterparts

� Reversible watermarking algorithms suffer from large runtime

requirements due to these complex mathematical operations

Efficient Implementation:

Parallel Processing� However, many algorithms are block-based, and their processing can

be done in parallel

� Multi-threaded programming enables efficient software

implementations of reversible watermarking algorithms to exploiting

this parallelizability

43


this parallelizability

� Multi-threaded implementation may be achieved by:

� Suitable computer architectures such as multi-core processors, GPU,

computer clusters

� Parallel processing provided by software such as MATLAB Parallel

Computing Toolbox, OpenCV Parallel Computing Descriptors, JAVA

Concurrency/Multithreading

Efficient Implementation:

Parallel ProcessingIDCT based Reversible Watermarking:

Divide into 8X8 blocks

44


Parallel Processing

L L

Efficient Implementation: On

FPGA� Hardware Implementation of the Watermarking Algorithms can be

extremely effective in improving the processing time

� Field Programmable Gate Array (FPGA) based implementations are

attractive because of:

� Lower cost

45


� Re-configurability

� Easy-to-use software provided by vendors

� Easy capabilities of interfacing with a PC

� Greater hardware and memory resources and dedicated arithmetic building

blocks for modern FPGAs

� Hardware implementations capable of meeting real-time constraints,

(if any)

Evaluation of Reversible

Watermarking Algorithms

� Parameters for evaluating watermarking algorithms performance:

� Embedding Capacity

� Cover Data Distortion

� Runtime Requirement

46


� Two-way evaluation

� Software Simulations

� Theoretical Analysis

Integer Transform based

Embedding Algorithm

Input: Cover image having m×n pixels, Watermark

Output: Watermarked image

1: For each consecutive pixel pair of the cover image

2: Compute average and difference by forward integer transform

Θ(mn/2) = Θ(mn)

47


transform

3: End


5: Embed next watermark bit into its difference

6: Obtain watermarked pixel pair by reverse integer transform

7: End

Θ(mn/2) = Θ(mn)

Θ(mn)

T = Θ(mn)

Integer Transform based

Extraction Algorithm

Input: Watermarked image having m×n pixels

Output: Retrieved image, Watermark

1: For each consecutive pixel pair of the watermarked image

2: Compute average and difference by forward integer transform

Θ(mn/2) = Θ(mn)

48


transform

3: End


5: Extract next watermark bit from its difference

6: Restore the difference

7: Retrieve original pixel pair by reverse integer transform

8: End

Θ(mn/2) = Θ(mn)

Θ(mn)

T = Θ(mn)

Data Compression based

Embedding AlgorithmInput: Cover image having m×n pixels, Watermark


1: For each pixel of the cover image

2: Apply L-level quantization to obtain quantized pixel value and quantization remainder

3: EndΘ(mn)

49


3: End

4: Losslessly compress the remainders

5: Concatenate the remainders and the watermark to produce the payload

6: For each quantized pixel q of the cover image

7: Obtain s = next L-ary symbol constituting the payload

8: Obtain watermarked pixel p = q + s

9: EndΘ(mn)

T = Θ(mn)

Data Compression based

Extraction AlgorithmInput: Watermarked image having m×n pixels


1: For each pixel of the watermarked image

2: Apply L-level quantization to obtain quantized pixel value and quantization remainder

3: EndΘ(mn)

50


3: End

4: Convert the L-ary remainders to the payload bit stream

5: Separate the payload bit stream into the compressed original remainders and the watermark

6: Restore the original remainders by lossless decompression

7: For each quantized pixel q of the watermarked image

8: Retrieve pixel p = q + (next L-ary original remainder)

9: End Θ(mn)

T = Θ(mn)

Histogram Bin Shifting based

Embedding AlgorithmInput: Cover image having m×n pixels, Watermark


1: Form frequency histogram freq[0..k] of the cover image

/*k is the number of grayscale levels*/

2: Find peak = mode of the frequency histogram

Θ(mn)

Θ(k)

51


2: Find peak = mode of the frequency histogram

3: For each pixel p of the cover image

4: If p > peak then p = p+1

5: Else If p = peak then p = p + (next watermark bit)

6: End

7: EndΘ(mn)

T = Θ(mn)+Θ(k) = Θ(mn) [[[[∵∵∵∵k<<mn]]]]

Θ(k)

Histogram Bin Shifting based

Extraction AlgorithmInput: Watermarked image having m×n pixels, peak


1: For each pixel p of the watermarked image

2: If p > peak + 1 then restore p = p − 1

3: Else If p = peak + 1 then

4: Restore p = p − 1

52


4: Restore p = p − 1

5: Extract next watermark bit = ‘0’

6: Else If p = peak then extract next watermark bit = ‘1’

7: End

T = Θ(mn)

Pixel Prediction based

Embedding AlgorithmInput: Cover image having m×n pixels, Watermark, Set (SL) of

selected pixel locations for embedding



2: If p belongs to SL then

3: p’ = p interpolated from its neighbors

53



4: e = p − p’

5: e’ = sign(e) × {|e| + (next watermark bit)}

6: p = p’ + e’

7: End

8: End

T = Θ(mn)

Pixel Prediction based

Extraction AlgorithmInput: Watermarked image having m×n pixels, Set (SL) of

selected pixel locations for embedding



2: If p belongs to SL then


54



4: e’ = p − p’

5: Extract next watermark bit = mod(e’,2)

6: Restore e =

7: Restore p = p’ + e

8: End

9: End

T = Θ(mn)

2

e'

Invertible IDCT based

Embedding AlgorithmInput: Cover image having m×n pixels, Watermark, Set (SL) of

locations within an 8×8 block selected for embedding


1: Divide the cover image into 8×8 blocks

2: For each 8×8 block B

3: X = IDCT of B

55


3: X = IDCT of B

4: For each element a of X

5: If location of a Є SL then

6: a = 2 × |a| + (next watermark bit)

7: End

8: End

9: B = Inverse IDCT of (modified) X

10:End

T = Θ(mn)

Invertible IDCT based

Extraction AlgorithmInput: Watermarked image having m×n pixels, Set (SL) of

locations within an 8×8 block selected for embedding


1: Divide the watermarked image into 8×8 blocks

2: For each 8×8 block B

3: X = IDCT of B


56



5: If location of a Є SL then

6: Extract next watermark bit = mod(a,2)

7: Restore x =

8: End

9: End

10: Retrieve B = Inverse IDCT of (restored) X

11:End

T = Θ(mn)

2

x

Time Complexities of Reversible

Watermarking Algorithms

� Algorithms belonging to the existing five broad classes of

reversible watermarking have a time complexity of Θ(mn)

� However in practice certain operations specific to some of the

classes involve complex mathematical steps, therefore they have

high runtime requirements

57


high runtime requirements

� For example IDCT

Future Research Directions

� Improving the efficiency of reversible watermarking algorithms in

terms of cover data distortion and embedding capacity

� Theoretic analysis of embedding capacity bounds and cover data

distortion characteristics of reversible watermarking algorithms

� Implementation of reversible watermarking algorithms on

58


� Implementation of reversible watermarking algorithms on

hardware to improve the runtime requirements of such algorithms

Bibliography� Cox I.J., Miller M.L., Bloom J.A., Fridrich J. and Kalker T., “Digital Watermarking

and Steganography”, Morgan Kaufmann Publishers, 2008.

� Feng J.B., Lin I.C., Tsai C.S. and Chu Y.P., “Reversible watermarking: current status and key issues”, International Journal of Network Security, vol. 2, no. 3, pp. 161–171, May 2006.

� Tian J., “Reversible data embedding using a difference expansion”, IEEE Transactions on Circuits Systems and Video Technology, vol. 13, no. 8, pp. 890–896, Aug. 2003.

� Tian J., “Reversible watermarking by difference expansion”, Proceedings of Workshop on Multimedia and Security, pp. 19-22, Dec. 2002.

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� Celik M.U., Sharma G., Tekalp A.M. and Saber E., “Lossless generalized-LSB data embedding”, IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 253–266, Feb. 2005.

� Celik M.U., Sharma G., Tekalp A.M. and Saber E., “Reversible data hiding”, Proceedings of International Conference on Image Processing, pp. III-157-III-160, Sept. 2002.

� Ni Z., Shi Y.Q., Ansari N. and Su W., “Reversible data hiding”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 3, pp. 354–362, 2006.

59

Bibliography� Luo L., Chen Z., Chen M., Zeng X. and Xiong Z., “Reversible image

watermarking using interpolation technique”, IEEE Transactions on Information Forensics and Security, vol. 5, no. 1, pp. 187–193, Mar. 2010.

� Yang B., Schmucker M., Funk W., Busch C. and Sun S., “Integer DCT–based reversible watermarking technique for images using companding technique”, Proceedings of SPIE, vol. 5306, pp. 405–415, 2004.

� Plonka G. and Tasche M., “Integer DCT-II by lifting steps”, International Series in Numerical Mathematics, vol. 145, pp. 235–252, 2003.

� Naskar R. and Chakraborty R. S., "Performance of Reversible Digital Image Watermarking under Error-prone Data Communication: a Simulation-based Study", accepted for publication in IET Image Processing.Study", accepted for publication in IET Image Processing.

� Naskar R. and Chakraborty R. S., "Reversible Watermarking Utilizing Weighted–median based Prediction", accepted for publication in IET Image Processing.

� Naskar R. and Chakraborty R. S., Das D. K. and Chakraborty C., “Digital Image Watermarking: Impact on Medical Imaging Applications in Telemedicine”, in Dr. R. Srivastava (ed.), “Recent Advances in Compute Vision and Image Processing: Methodologies and Applications”, IGI Global(forthcoming).

� Naskar R. and Chakraborty R. S., “Fuzzy Inference Rule based Reversible Watermarking for Digital Images”, accepted in International Conference on Information Systems Security (ICISS) 2012, Guwahati, India. To be published in Lecture Notes on Computer Science. 60

Bibliography� Naskar R. and Chakraborty R. S., " Lossless Secret Image Sharing based on

Generalized–LSB Replacement", accepted in ACM Research in Applied Computation Symposium (RACS), San Antonio, Texas, USA, 2012.

� Naskar R. and Chakraborty R. S., "Reversible Image Watermarking through Coordinate Logic Operation based Prediction", International Conference on Information Systems Security (ICISS) 2011, Kolkata, India. Published in Lecture Notes on Computer Science, vol. 7093, pp. 190-203, 2011.

� Naskar R. and Chakraborty R. S., "Lossless Data Hiding for Halftone Color � Naskar R. and Chakraborty R. S., "Lossless Data Hiding for Halftone Color Images", International Conference on Image Information Processing (ICIIP) 2011, Shimla, Himachal Pradesh, India.

� Bandyopadhyay S., Naskar R. and Chakraborty R. S., “Reversible Watermarking Using Priority Embedding through Repeated Application of Integer Wavelet Transform”, International Conference on Security Aspects in Information Technology, High-performance Computing and Networking (InfoSecHiComNet) 2011, Haldia, West Bengal, India. Published in Lecture Notes in Computer Science, vol. 7011, pp. 45-56, 2011.

� Bandyopadhyay S., Naskar R. and Chakraborty R. S., "Reversible Digital Watermarking using Integer Wavelet Transform", International Conference on Scientific Paradigm Shift in Information Technology and Management (SPSITM), 2011, Kolkata, India. 61

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