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2. 2 u Copyright Protection u Authentication of multimedia data u Robust Data Hiding and Security issues

3. 3 ¨ There are three different applications that use digital watermarking in images. 1- The detection of the presence of the embedded signal 2- The concealment of the bit stream in the image and the extraction of this information. 3- The ability to reveal whether some sort of changes was done to the image. ¨ In the last application one of the characteristics of the hidden data should be low robustness, while in the first two high robustness of the embedded data is one of the most important requirements for successful data hiding. ¨ The first applications is implemented in the Method 1and the second one is implemented in the Method 2 of this project

4. 4 u Digital watermarking is a technique for data hiding in digital multimedia u WM in images should meet the next set of requirements : * Invisibility to a Human Visual System * Robustness to various kinds of distortions * Simplicity of detection and extraction to the owner * High information capacity

5. 5 u Watermarking techniques divided into two basic classes : * Spatial Domain WM- pixels values are affected * Frequency Domain WM- transform coefficients are affected u In this project the second technique is investigated u The advantages are high robustness and exploiting the Human Visual System features u The watermark is embedded using two transforms * DCT based watermark * DFT based watermark

6. 6 u DCT transform algorithm is given by u The advantages of DCT are * Real coefficients and relatively small calculation time * Small manipulations on coefficients don’t affect the image visual properties

7. 7 u DFT transform algorithm is given by u The advantages of DFT are * Most of the image information is contained in phase that allows great flexibility on magnitudes manipulations * Fast FFT algorithm decreases computation time

8. 8 u The project scheme contains the following blocks : * Encoder - embeds the WM into the image * Channel - corrupts the watermarked image * Decoder - detects the WM sequence from corrupted watermarked image

9. 9 u The following diagram demonstrates the above :

10. 10 u The encoding schemes uses following characteristics : * Choice of WM amplitude has an important impact on the visibility of the WM * Selection of effected coefficients in the transformed image has to preserve the tradeoff between the robustness to corruption and the invisibility of the WM * WM embedded sequence is a set of normally distributed numbers chosen among X pseudo-random sequences

11. 11 u The following diagram demonstrates encoder block diagram u The affected coefficients are chosen according to the transform and the parameters specified above

12. 12 u The following diagram demonstrates channel block diagram

13. 13 u The following diagram demonstrates decoder block diagram

14. 14 u WM embedder performs zig-zag scan on the DCT coefficients - an approximation of radial frequency u M diagonals starting from L-th are selected to vector T u New vector T’ is created by the following rule:

15. 15 u New vector T’ is reinserted in the appropriate indices u Inverse DCT is performed to create watermarked image

16. 16 u The following images present the embedding of the WM in Lena. Original ImageImage with WM

17. 17 u The WM bellow was created with following parameters :  WM amplitude:  = 0.2 * width of WM: M = 50 diagonals * first diagonal : L = 165 diagonal u The detection occurred on uncorrupted image 02004006008001000 0 0.5 1 1.5 2 Detection watermarks response correlation threshold Embbeded coefficents

18. 18 u Two types of corruption are presented - AWGN and JPEG AWGN Corrupted Watermarked Image 02004006008001000 0 0.5 1 1.5 2 Detection watermarks response correlation threshold

19. 19 Corrupted Watermarked Image-JPEG comp. 01002003004005006007008009001000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Detection watermarks response correlation threshold u JPEG compression (quality of 50) is a strong noise but the WM signal can still be detected

20. 20 u Likely to DCT, DFT WM embedder selects square rings around DC after shift of FFT transform coefficients to the center u M squares starting from L-th are selected to vector T u New vector T’ is created by the following rule:

21. 21 u As mentioned above, only magnitude of coefficients are modified in the way that preserves the symmetry of the real image DFT u New vector T’ is reinserted in the appropriate indices u Inverse DFT is performed to create watermarked image

22. 22 u The following images present the embedding of the WM in Lena. Original ImageImage with WM

23. 23 u The WM bellow was created with following parameters :  WM amplitude:  = 0.17 * WM width : M =20 rings * first square: L = 45 ring u The detection occurred on uncorrupted image Embbeded coefficents 01002003004005006007008009001000 0 50 100 150 Detection watermarks response correlation threshold

24. 24 u Two types of corruption are presented - AWGN and JPEG 01002003004005006007008009001000 0 10 20 30 40 50 60 70 80 90 100 Detection watermarks response correlation threshold AWGN Corrupted Watermarked Image

25. 25 u JPEG compression (quality of 50) is a strong noise but the WM signal can still be detected JPEG-Corrupted Watermarked Image 01002003004005006007008009001000 0 50 100 150 200 250 300 350 400 Detection watermarks response correlation threshold

26. 26 u Both of the presented transforms perform robust, invisible and easy to extract WM technique. u Choice of suitable parameters must be taken into consideration, when these two transforms are compared u Tradeoff must be achieved between the invisibility and robustness when M, L and  parameters are chosen u On the whole, no significant advantage was found to recommend on DFT or DCT.

27. 27 u Exploiting of characteristics of Human Visual System: u Visual masking - every pixel is changed according to its local weighting factor u Image partition to blocks in order to enable efficient implementation and real time processing u Choice of different set of the parameters for different images

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29. 29 u This method exploits WM application, in which, bit stream is the hidden signal u Unlike first technique, encoding and detection is based on FFT signaling scheme, similar to communication channel, where narrow band signal is hidden in wide band environment u This method is a relatively new in the field and has a lot of unexplored features

30. 30 u Similar to Method 1 the main hierarchy includes encoder, channel and decoder. u The difference is in the way that spectral components are computed and affected u The image is divided to blocks and selected transform is applied on each block u The size of the block is a parameter of the encoding scheme

31. 31 u Encoder performs the following algorithm u Blockwise spectral transform - DFT/DCT u Selection of coefficients with unique magnitude in case of DFT - only half of the coefficients are selected u Alignment of each block to one column vector u KLT transform is performed on each vector u All vectors are combined to Mc matrix u Mapping of the binary stream to the signature u Insertion of the signature u Inverse process to obtain the encoded image

32. 32 u The image coefficients are highly correlated and this fact can damage the WM invisibility u An efficient way to reduce this correlation is to apply KLT on each vector of block coefficients u KLT matrix is calculated once from 15 different pictures - For each image, Mc matrix is calculated by applying blockwise DCT/DFT Mc matrices are merged to one matrix X. Matrix X is multiplied by its transpose matrix X’. The result is normalized by number of columns in X to obtain a square matrix Rx. The Eigen vectors of Rx are calculated and combined to KLT matrix T.

33. 33 u The following figure demonstrates the creation of KLT matrix

34. 34 u The following figure demonstrates the structure of the encoder

35. 35 u The insertion of the binary stream should be done with the highest efficiency and without damaging the image u The binary stream should be mapped to the signature that has high self correlation u In addition, the signature has to be low correlated with the original image in order to achieve high detection ability u The binary stream is divided to p-length segments u p is a parameter that is set in the initialization of the encoder

36. 36 u In order to create high correlated sequence to map p-length segment the following algorithm should be performed  Create a random binary (±1) sequence b k of length 2 p-1.  Calculate B k - DFT of b k.  Calculate S k from B k such that | S k | = 1 and  S k =  B k  Take IDFT of S k to obtain s k that is real, cyclic all pas function. All cyclic shifts of s k and their negative are orthogonal.  Create a table of all cyclic shifts and their negative (MSS table) to map binary sequence length p.

37. 37 u Binary sequence divided to p-length fragments u For each segment decimal representation Dk is calculated and entry in MSS table that is appropriate to Dk is selected u All selected sequences combined into one row vector to generate the signature u The value of p has an influence on the BPP (Bit Per Pixel) rate and on the robustness of the encoded signal u The larger p is the lower the BPP goes but the robustness of the signal increases

38. 38 u The BPP is given by the following formula u It is possible to adjust the BPP in more precise way by using different coding schemes, like checksum and Reed Solomon

39. 39 u The detection is made without resorting to the original image, therefore the components of the original image are an additive noise for the embedded signal u Some pre-processing is required to improve the detection efficiency - self noise suppression u The main idea is to estimate the original picture and to subtract this prediction from the received image Error = D(I rec ) = I rec - I est u The buried information can be extracted more efficiently from Error rather then from received image itself

40. 40 u The estimation too similar to received image will produce zero and this will make it impossible to detect the hidden signal u On the other hand very coarse prediction will make the detection very difficult because of large amount of noise u Some sort of quantization of the received image may by used as the estimated image u The step of the quantizer used will depend on the level of noise

41. 41 u Not any modification of the image coefficients allowed u The error introduced should maintain the following condition e = |C ^ – C | = | s – D(C) | where C ^ is the encoded image and C is an original one u The algorithm for embedding the signature sequence s in c is if (|s(k) – D(c(k)| < v(k)) then e(k) = s(k) – D(c(k)) else e(k) = v(k)sign(s(k) – p(k)) if ( rem(abs(c(k) >  /2) then e(k) = -e(k) else e(k) = e(k) if ( c(k)  0) then C ^ (k) = c(k) + e(k) else C ^ (k) = c(k) – e(k ) u v(k) is the visual threshold calculated for each block and determines the activity a particular block

42. 42 u The detected signature s dec is extracted from the spectrum coefficients of received image after KLT transform by applying Self-Noise suppression scheme u If the encoded image was not corrupted the s dec is equal to an original signature s u After the extraction the signature s dec is divided into 2 p-1 - length fragment when each one is representing p-length binary segment u There are 2 p possible sequences for each fragment

43. 43 u Each fragment represents p-bit number u For each fragment the correlation is calculated with every entry in MSS table u The index of the entry that produced maximum correlation is the decimal representation of p-length segment

44. 44 u The following figure summarizes the decoder

45. 45 u The following images demonstrates the encoding scheme results when the applied transform is DFT,p = 9 and delta = 140

46. 46 u The following images demonstrates the encoding scheme results when the applied transform is DCT,p = 8 and delta = 20

47. 47 u The system was checked with 5 256x256 standard images u The following table summarizes the results of the project u The following table summarizes the BER of the project when the images were compressed with JPEG ( Quality = 75)

48. 48 u The article results report on achieved capacity of 1100 bits per image for ‘Lena’, which matches achieved capacity in this project u DCT and DFT results show a very similar robustness and visual properties of the watermarked image u DFT has less coefficients, therefore the amount of the embedded data is smaller u Larger block size enables better detection u On the whole, results of both articles investigated, were achieved and confirmed

49. 49 u Selection and manipulation on the part of the coefficients will improve the robustness u Different error insertion for each spectral component will enhance the invisibility of the watermark u Interleaving of the coefficients before the encoding and de- interleaving before the decoding will prevent from some kinds of “spike” noise like JPEG to destroy the signature u Different encoding schemes like Reed Solomon, Checksum will reduce the bit rate but will reduce the number of errors as well

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