Secure Spread Spectrum Watermarking for Multimedia Young K Hwang

The characteristics of watermark Unobtrusiveness Robustness Common signal processing Common geometric distortion Collusion and forgery Universality Unambiguousness

Design for a strong watermark Watermark structure i.i.d. samples from Gaussian distribution Insertion strategy Embedded in perceptual frequency area For a watermark to be robust and secure, these two components must be designed correctly

Watermark in the frequency domain

Spread Spectrum Coding of a Watermark The watermark is spread over many frequency bins The location of watermark isn’t obvious Sufficient small to be undetectable Is it possible to verify watermark? The owner know where the watermark is Increase the energy at a particular freq to detect the watermark.

Cox’s scheme: Embedding

Cox’s scheme: Detecting

Watermark procedure D : each document V= : a seq of D to be inserted by wmk X= : a watermark to be inserted V’= : watermarked sequence D’ : watermarked document : attacked document : attacked watermarked seq of : attacked watermark

Inserting the watermark X V = V’ Three formulae for computing V’ (1) (2) (3)

Extracting the watermark Find the inverse function of inserting watermark. (2)->

Choosing the length of the Wmk The choice of n indicates the degree to which the watermark is spread out among the relevant components of the image. Proper value of n makes it easy to identify the watermark. Too large value of n -> distort the image. Too small value of n -> cause robust problem

Evaluating the similarity of wmk The similarity of X and X* can be measured by To decide whether X and X* match, one determines if Sim(X,X*) > T, where T=some threshold T : chosen to minimize the prob of both false alarm and miss detections.

Evaluating the similarity of wmk Cont. Creator of X* has no information on X. X* is created independently to X. For fixed any, each will be independently distributed according to N(0,1) ~ Thus, Thus, false alarm prob doesn’t depend on n But, the large n increases the value of similarity func.

Questions Why does large value of n increase similarity function when X and X* are correlated?

Post-processing options x i should be detected by similarity after many kinds of signal processing. Some processes make it hard to detect watermark due to severely distorting watermark (for example, D/A-A/D, dithering process) Setting T to low value result in increasing false alarm prob. A method to increase sim(X,X*) is required for some processes

Robust statistics for a specific X*(distortion version of X) Goal : Increase increase decrease Method 1) Method 2) Method 3)

Question on previous slide Can such postprocessing steps affect the false positive probability? According to Cox’s paper, that process doesn’t affect the statistical significance calculation as long as X* depends on D* and D.

Resilience to Multiple Document (Collusion) Attacks The most general attacks consists of using t multiple watermarked copies D 1 '...,D t ' of document D to produce an un- watermarked document D*. If the i-th watermark is the same for all copies of the document then it cannot be detected, changed or removed.

Robust, secure, invisible watermark, resistant with respect to the collusion attack (averaging copies of documents with different marks). Collution attack cont. Marking copies of one document with a customer signature. … W1W1 W2W2 WNWN N customers … + original

Experimental Results Response of the watermark (ROW)=32.0

Experiment 1: Uniqueness of Watermark

Experiment 2: Image Scaling To recover the watermark the quarter sized image was rescaled to its original dimension, Fig. 7b.(ROW=13.4, 75% of the original data is missing)

Experiment 3: JPEG Coding Distortions Here are two JPEG encoded versions of the Bavarian couple Image with different percentages for the quality and smoothing. ROW=22.8, 13.9 respectively

Experiment 4: Dithering Distortion ROW=5.2, 10.5 with a postprocess

Experiment 5: Cropping Cropping involves the cutting out and removal of portions of an image.(ROW=14.6, 75% of the original date is removed

Experiment 6: Print, Xerox, and Scan This image represents the result after it has gone through the 4 stage process, printing, xeroxing, scanning and rescaling. ROW= with a postprocess

Conclusion A need for electronic watermarking is developing as electronic distribution of copyright material becomes more widespread. This paper outlined the necessary characteristics of a watermark Fidelity preservation Robustness to common signal and geometric processing operations Robustness to attack Applicability to audio, image and video data.

Conclusion … continued Using the Bavarian couple image, the algorithm used can extract a reliable copy of the watermark from imagery that was degraded with several common geometric and signal processing procedures. These procedures include translation, rotation, scale change, and cropping. The algorithm displays strong resilience to lossy operations. Finally, this proposed methodology is used to hide watermarks in data, the same process can be applied to sending other forms of message through media data.