Digital watermarking: algorithms and applications

1. Digital watermarking: algorithms and applications Park, Jungjin

2. Index • Watermarking embedding • Watermarking detection • Document • Graphic • Audio • Video • Image

3. Watermark embedding Watermark embedding scheme ► embed the watermark directly into the host data or to a transformed version of the host data (DCT, wavelet)-popular due to the natural framework for incorporating perceptual knowledge into the embedding algorithm -Many of compression techniques such as JPEG work in the same framework and this allows for watermarking of the compressed bit stream with only partial decoding

4. Watermark embedding S: original host signal (image luminance values or DCT coefficients) M: watermark message (serial number or credit card number logo) K: secret key

5. Watermark embedding ► Secret key is used to generate a random sequence to embed in the host signal ► It is also used to determine a random sequence which identifies locations in the host signal for watermarking embedding -without knowledge of the key, it should be difficult to remove or alter the embedded message without destroying the original content ► no-key or public-key (QIM) may be desirable .

6. Watermark detection Detection or verification : the process of making a binary decision at the decoder, it check whether a specific watermark is or not present in the received data ▪ Type I (false positive) : the case where a watermark is detected when it does not exist ▪ Type 2 (false negative) : the case when a existing watermark is not detected Identification : the process of being able to decode one of N possible choices at the receiver. ▪ Open set : the possibility that one of N or no watermark exists in the data ▪ Closed set : the problems where one of N possible watermarks is known to be in the received data and the detector has to pick the most likely one

7. Watermark detection Blind detection ►S is not available at the decoder, →S acts as an additive noise component in the watermarking detection process ►S is available at the decoder →It could be used to estimate the channel distortions and invert them to provide better detection performance

8. Watermark detection Typical watermarking detector Watermark detection is performed by comparing the correlation coefficient to a threshold value which can be modified according to the tradeoff between probability of detection and the probability of false alarm Watermark W detected Watermark W is not detected

9. Documents watermarking Document watermarking can be achieved by altering the text formatting or by altering certain characteristics of textual elements Line- Shift Coding ►The most easily discernible by readers ►The most robust type of encoding in the presence of noise →the long lengths of text lines provide a relatively easily detectable feature ►Altering a document by vertically shifting the locations of text lines ►decoding without need of the original image →Original image is known to have uniform line spacing

10. Documents watermarking Word-Shift Coding ► Altering a document by horizontally shifting the locations of words within text lines ► The spacing between adjacent words on a line is often varied to support text justification. →less discernible to the reader than line-shifting ► Decoding need the original image →variable spacing

11. Documents watermarking Feature coding ► Chosen text features are altered by extending or shorting the lengths by one or more pixels ► Decoding require the original image Ex) vertical end line top of letters, b,d,h,etc Altered by expending or shorting lengths

12. Graphics watermarking Watermarking of facial animation parameters (FAP) defined by the MPEG-4 standard ► 66 FAPs ▪ global head motion parameters - Head pitch and yaw angles ▪ local face motion parameters -opening of eyelids , opening of lips, movement of innerlip corners 16 FAPs (jaw, chin, inner lips and cornerlips) 12 FAPs (eyeballs pupils eyelids), 8 FAPs eyebrows , 4 FAPs cheeks , 5 FAPs tongue , 3 FAPs global head rotation, 10 FAPS outer lip position, 4 FAPs nose, 4 FAPs ears

13. Graphics watermarking Embedding ► One bit of watermark information is embedded in a block of facial animation parameters (FAPs) -using PN sequence →generated by any random number generator that produces binary output values -1 and +1) ► Minimize visible distortion -apply an amplitude adaptation Limit the maximum deviation of the watermarked FAPs from the unwatermarked FAPs to 3% of dynamic range for local FAPs like lip movement, and 1% of the dynamic range for global FAPs like head rotation.

14. Graphics watermarking Detection ► Extracted from the watermarked parameters directly by →Subtraction of the unwatermarked FAPs from the watermarked FAPs →Subsequent correlation with the same filtered PN sequence that has been used for embedding →Thresholding as a bit decision

15. Video watermarking Current issue ► Design of an effective copy control system for DVD include s the placement of the detector Two proposals for detector placement ▪ Watermark detection in the drive →Advantage : Pirated content cannot leave the drive in playback mode or recording mode ▪ Watermark detection within the application →Advantage : ability to provide a more complex detector and flexibility of extending the scheme to other data type

16. Video watermarking Unique requirement for DVD application ► Copy generation management Ability to detect the copy once state and change it to copy no more state after the recording ►Two approach Secondary watermarks, Ticket

17. Video watermarking Scene-adaptive video watermarking technique ► based on temporal wavelet transform ► using a tow-band perfect reconstruction filter bank →Separates static areas from dynamic areas so that separate watermarking strategies can be applied to the different areas. ► constant watermarking apply for static, varying watermark apply for the dynamic areas to defeat watermark deletion through frame averaging

18. Video watermarking Real time watermark embedding of compressed video ► adding the watermark by modifying the fixed length and variable length codes in the compressed video bit stream → allow for a computationally efficient way of real-time watermark insertion → allow for a relatively high payload ►drawback: decoding the bit stream removes the watermark

19. Video watermarking ► More robust technique for real time watermark embedding →adding the watermark by enforcing energy differences between various video regions → This technique is done by discarding high frequency components →only partial decoding of a compressed video bit stream is necessary to apply this watermark

20. Audio watermarking Audio watermarking requirements ► Inaudible ► Robust :filtering, resampling, compression, noise, cropping, A/D-D/A conversion ► Embedded directly in the data ► self-clocking for ease of detection in the presence of cropping and time-scale change operations

21. Audio watermarking Phase coding Work by substituting the phase of an initial audio segment with a reference phase that represents the data For the decoding process The synchronization of the sequence is done before decoding The length of the segment and the data interval must be known at the receiver The value of the phase of segment is detected as a binary string

22. Audio watermarking Spread spectrum ► Direct Sequence Spread Spectrum encoding(DSSS) →spreads the signal by multiplying it by a chip(key), a maximal length pseudorandom sequence- applied to the coded information to modulate the sequence into a spread spectrum sequence →The spectrum of the data is spread over the available band →the spread data sequence is attenuated and added to the original file as additive random noise ►decoder →pseudorandom key(chip) is needed to decode →signal synchronization is done

23. Audio watermarking ►Unlike phase coding, DSSS introduced additive random noise to the sound ►to keep the noise level low, inaudible →The spread code is attenuated to roughly 0.5 percent of the dynamic range of the host sound file

24. Audio watermarking Echo data hiding ► Embedding data into a host audio signal by introducing an echo -data are hidden by varying three parameters of the echo Initial amplitude, offset, decay rate Zero represent a binary zero ,one represent a binary one < threshold (human ear can resolve the echo) ► It is possible to encode and decode information in the form of binary digits into a media stream with minimal alteration to the original signal ►to minimize alteration → Addition of resonance simply gives the signal a slightly richer sound

25. Image watermarking Embed m-sequences into the least significant bit (LSB) of the data ► provide an effective transparent embedding technique ► good correlation properties (for detection) ► computationally inexpensive to implement Texture block coding ► Hide data within the continuous random texture patterns of a picture ► Implemented by copying a region from a random texture pattern found in a picture to an area that has similar texture

26. Image watermarking • Texture block coding Detection • Autocorrelate the image with itself. This will produce peaks at every point in the autocorrelation where identical regions of the image overlap. • 2. Shift the image as indicated by the peaks in Step 1. • Now subtract the image from its shifted copy • 3. Square the result and threshold it to recover only those values quite close to zero. The copied region will be visible as these values.

27. Image watermarking Transform domain watermarking ► robust to common compression techniques ► block-based DCT which is the fundamental building block of current image coding standard JPEG and MPEG ► a pseudorandom subset of the blocks are chosen and a triplet of midrange frequencies are altered to encode a binary sequence ▪ Watermarks inserted in the high frequencies are vulnerable to attack ▪ The low frequency components are perceptually significant and sensitive to alterations

28. Image watermarking ►two watermarking techniques based on visual models ▪ Image-adaptive DCT approach ▪ Image-adaptive DWT approach ► Utilizing visual models which have been developed in the context of image compression ► Very effective visual models have been developed for compression applications that take into account frequency sensitivity, local luminance sensitivity, contrast masking

29. Image watermarking Visual model ►frequency sensitivity : human eye’s sensitivity to sine wave gratings at various frequencies ▪depend on the modulation transfer function of the eye and is dependent of the image data ►Luminance sensitivity : measure the effect of the detectability threshold of noise on a constant background ►Contrast masking : the detectability of one signal in the presence of another signal and the effect is strongest when both signals are of the same spatial frequency , orientation, and location ►combination of the components –JND thresholds for the entire image

30. Image watermarking IA-DCT embedding DCT coefficient Watermarked DCT coefficients Sequence of watermark values Computed JND from the visual model ►The watermark is only inserted into the luminance component of the image

31. Image watermarking Detection Normalized correlation detection scheme based on classical detection for the IA-DCT scheme Received watermark Normalized correlation coefficient between two signals

32. Image watermarking IA-W embedding Wavelet coefficient at position (u,v) in resolution level l, frequency orientation f Watermarked wavelet coefficient Computed frequency weight at level l and frequency orientation f Watermark sequence ► watermark is inserted only in the luminance component of the image

33. Image watermarking IA-W detection Correlation is performed separately IA-W scheme is based on a much simpler visual model which only takes into account frequency sensitivity, the multi resolution structure of the watermark and the watermark detection scheme results in a very robust scheme

34. Image watermarking Digital watermarking by geometric warping Embeds information in an image by changing the geometric features of the image ►the watermark is formed by a predefined dense pixel pattern, such as a collection of lines ►Salient points in an image are warped into the vicinity of the line pattern in such a way that the changes to the image are imperceptible ▪ subdivide the image in a number of blocks. Find a fixed number of most significant pixels, these are called salient points

35. Image watermarking Detection ►Determining whether a significantly large number of points are within the vicinity of the line patterns Advantage ►detection is computationally faster ►Easier to detect the watermark in images have been rotated, scaled, or distorted by a geometric transformation

36. Questions?