The document discusses an efficient digital watermarking system based on the discrete cosine transform (DCT) to protect the integrity of digital content such as images. It outlines the theoretical foundations, implementation techniques, and future work needed to enhance the watermarking process against various attacks like noise and compression. The conclusion emphasizes that DCT domain watermarking offers more robustness than spatial domain methods, making it a critical approach for safeguarding digital media.

1. A DCT-DOMAIN SYSTEM FOR ROBUST IMAGEWATERMARKING
Prepared by:-
DineshThakur
Nisarg Shah
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2. Outline
• Background
• Theoretical Understanding
• Implementation
• Future Work
• Conclusion
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3. Background
• Digitalization unfolds its boundary at every phase and into all applications.
• Protecting the integrity and originality of data confined in the form of audio, video, text and images
is the need of the hour.
• Cryptography, Steganography,Watermarking are information hiding technique.
• Watermarking is emerged as one of the technological solution to provide ownership of content.
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4. Background
Application of watermarking
• Copyright
The objective is to permanently and unalterably mark the image so that the credit or assignment is
beyond dispute.
• Digital Rights
A file may only be used by users with a license that matches the watermarked signature.
• Information Hiding
Foil counterfeiters
• Meta-tagging
Store keywords, descriptions, time along with images
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5. Background
Steganography vs.Watermarking
• Steganography is to hide a message m in data such eavesdropper cannot detect the presence of m
in d.
• Watermarking is to hide a message m in data d, to obtain new data s0 an eavesdropper cannot
remove or replace m in d.
Cryptography vs.Watermarking
• Methods to protect data owners against unauthorized copying and illicit distribution can be done by
watermarking.
• Encryption systems do not completely solve the problem, because once encryption is removed there
is no more control on the airing of data
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6. Background
TYPES OF DIGITALWATERMARKING
Fig 1:-Type ofWatermarking
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7. Background
IdealWatermarking
• Imperceptibility : Demographically and everlasting invisible so that data quality is not degraded.
• Easily extractable: Owner of data should easily excerpt it.
• Robust: Irrespective of the various technique applied the watermarked image like shouId be defiant
towards it. Attempt to destroy must leave a visible deterioration on image.
Fig 2:-
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8. Theoretical Understanding
• C(u)=.707 for u=0 , 1 for elsewhere, same for C(v).
• Represents data via summation of variable frequency cosine waves.
• Compute the only true value of function.
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9. Theoretical Understanding
• Energy Compactness
• Decorrelation
• Separable function
• Orthogonality
• Quantization is lossy element not transformation.
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10. Theoretical Understanding
Fig 3:- Co-relation between image before and after DCT [11]
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11. Theoretical Understanding
Fig 4 :- Energy Compactness of different transformation[7]
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12. Theoretical Understanding
Fig 5 :- Comparison of DCT vs DFT[8]
• DFT assumes that each block is repeated having the same value n pixel away.
• DCT assumes that the pixel is identical just next to it with periodicity 2N with
mirror symmetry3/30/2017 ELG 5378 12

13. Theoretical Understanding
• Divide the image because exploit more redundancy compared to the whole image.
• Better for hardware realization and can have parallel processing.
• Zig zag component refers to picking up the low frequency component first and then the higher
frequency.
• Psychovisual consideration found we are more sensitive toward low spatial frequency.
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14. Theoretical Understanding
• Truncation of higher spectral coefficients results in blurring when the details are high.
• Variance from block to block lead in artifacts creating checker board effect.[9]
• Coarse quantization of some of the low spectral coefficients introduces graininess in the smooth
portions of the images.
• So beyond a bit rate we can’t use DCT.
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15. Implementation
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16. Implementation
matrix = zeros(300,500);%[Height=300,Width=500]
matrix(100:250,100:350)=1;
figure,imshow(matrix);
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17. Implementation
image1 = image(:,:,1);
dimage1 = dct2(image1);
dimage11 = dimage1;
dimage1(1:rows,1:columns) =
dimage1(1:rows,1:columns) + strength * m ;
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18. Implementation
• Strength is defined as maximum allowable weight for an invisible conversion to the (i,j) DCT co-
efficient.[4]
• Fi & Fj are the vertical and horizontal spatial frequency.
• R is set to .7
• Tmin is the minimum value for a given fmin .
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19. Implementation
copy_image1 = idct2(dimage1);
copy_image(:,:,1) = copy_image1;
figure,imshow(copy_image/255)
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20. Implementation
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21. Implementation
copy_image1 = dct2(copy_image(:,:,1));
copy_image1(1:rows,1:columns) =
copy_image1(1:rows,1:columns) - strength *
m;
copy_image11 = idct2(copy_image1);
yy(:,:,1) = copy_image11;
figure,imshow(yy/255);
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22. Implementation
figure,imshow(abs(yy-image)*10000);
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23. FutureWork
1) Testing the DCT domain Watermarking under the following conditions:
• JPEG Compression
• Cropping
• Gaussian Noise
2) Using combined DCT &DWT watermarking methods for better results :
• DCT and DWT domain watermarking combined can give us the better result.
• DCT domain watermarking can survive against the attacks such as noising, compression , sharpening.
• DWT uses embedded zero-tree wavelet (EZW) image compression scheme and high frequency sub bands as
LH,HL,HH etc.
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24. Conclusion
• Digital watermarking is one of the keys for protecting the integrity and authenticity of the digital
content(audio , video , text , image etc..).
• It focuses on embedding information inside a digital object such that tampering with the watermark or
otherwise altering a watermarked object should always be detectable, and attempting to remove a
watermark from its object should make the object useless.
• DCT domain watermarking is comparatively much better than the spatial domain watermarking .
• DCT domain watermarking is more robust as it can survive against the attacks such as noising,
compression, sharpening and filtering and also use JPEG compression method.
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25. References
[1]. Potdar, Vidyasagar M., Song Han, and Elizabeth Chang. "A survey of digital image watermarking
techniques." Industrial Informatics, 2005. INDIN'05. 2005 3rd IEEE International Conference on. IEEE, 2005.
[2]. Barni, Mauro, et al. "A DCT-domain system for robust image watermarking." Signal processing 66.3
(1998): 357-372.
[3]. Lee, Sin-Joo, and Sung-Hwan Jung. "A survey of watermarking techniques applied to
multimedia." Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on. Vol. 1.
IEEE, 2001.
[4]. Hernandez, Juan R., Martin Amado, and Fernando Perez-Gonzalez. "DCT-domain watermarking
techniques for still images: Detector performance analysis and a new structure." IEEE transactions on image
processing 9.1 (2000): 55-68.
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26. Reference
[5]. Wong, Ping Wah, and Nasir Memon. "Secret and public key image watermarking schemes for image
authentication and ownership verification." IEEE transactions on image processing 10.10 (2001): 1593-
1601.
[6]. Al-Haj, Ali. "Combined DWT-DCT digital image watermarking." Journal of computer science 3.9
(2007): 740-746.
[7] Nptelhrd. "Lecture - 15 Discrete Cosine Transform." YouTube. YouTube, 15 Oct. 2008. Web. 28 Mar.
2017. <https://www.youtube.com/watch?v=S8FkaEWfCOg&t=449s>.
[8] Alfred936. "Digital image processing: p010 - The Discrete Cosine Transform (DCT)." YouTube.
YouTube, 15 Mar. 2013. Web. 12 Mar. 2017.
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27. Reference
[9] "Compression artifact." Wikipedia. Wikimedia Foundation, 21 Mar. 2017. Web. 23 Mar. 2017.
[10] "Discrete Cosine Transform." Discrete Cosine Transform. N.p., 12 Jan. 2014. Web. 28 Mar. 2017.
[11] "The Discrete Cosine Transform (DCT) | Mathematics of the DFT." DSPRelated.com | DSP. N.p.,
26 May 2013. Web. 28 Mar. 2017.
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28. Thank you.
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