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Spread Spectrum Watermarking

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Spread Spectrum Watermarking. Multimedia Security. Spread Spectrum Communication. A Narrow-band signal is transmitted over a much larger bandwidth such that the signal energy presented in any signal frequency is undetectable. Watermarking (1/3). - PowerPoint PPT Presentation
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Spread Spectrum Watermarking Multimedia Security
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Page 1: Spread Spectrum Watermarking

Spread Spectrum Watermarking

Multimedia Security

Page 2: Spread Spectrum Watermarking

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Spread Spectrum Communication

• A Narrow-band signal is transmitted over a much larger bandwidth such that the signal energy presented in any signal frequency is undetectable.

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Watermarking (1/3)

• A watermark is spread over many frequency bins so that the energy in one bin is very small and certainly undetectable.

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Watermarking (2/3)

• Because the watermark verification process knows the location and content of the watermark, it is possible to concentrate these weak signals into a single output with high SNR.

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Watermarking (3/3)

• Remark:To destroy such a watermark would require noise of high amplitude to be added to all frequency bins.

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Why it is secure?

• The location of the watermark is not obvious

• Frequency regions should be selected that ensures degradation of the original data following any attack on the watermark

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Why/How it is imperceptible? (1/2)

• The energy in the watermark is sufficiently small in any single frequency coefficient

• It is possible to increase the energy present in particular frequencies by exploiting knowledge of masking phenomena in human auditory and visual systems

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Why/How it is imperceptible? (2/2)

• Upon applying a frequency transformation (FFT/DCT) to the data, a perceptual mask is computed that highlights perceptual significant regions in the spectrum that can support the watermark without affecting perceptual fidelity.

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Why it is robust? (1/2)

• To be confident of eliminating a watermark, an attack must attack all possible frequency bins with modifications of certain strength → create visible defects in the data

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Why it is robust? (2/2)

• Unintentional signal distortions due to compression or image manipulation, must leave the perceptually significant spectra components intact, otherwise the resulting image will be severely degraded.

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Spread Spectrum Watermarking (1/2)

• In order to place a length n watermark into an N×N image, the N×N DCT of the image is computed and watermark is placed into the n highest magnitude coefficients (which are data dependent) of the transformed image, excluding the DC component (not necessary).

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Spread Spectrum Watermarking (2/2)

• A watermark consists of a sequence of real numbers X = x1 , … , xn , where each v

alue xi is chosen independently according

to N(0,1) : normal distribution assumption

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Watermark Insertion (1/2)

• When we insert X into V to obtain V ’ we specify a scaling parameter α, which determines the extent to which X alters V.

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Watermark Insertion (2/2)

(1)vi’ = vi + α xi

(2)vi’ = vi (1 +α xi ) = vi +α xi vi

(3)vi’ = vi ( e α x i ) or log vi’ = log vi + α xi

• (1) may not be appropriate when the values of vi varying widely!!

• Insertion based on (2) or (3) are more robust against ‘different in scales.’

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Determination of α (1/5)

• A single α may not be applicable for perturbing all of the values vi , since

different spectral components may exhibit more or less tolerance to modification

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Determination of α (2/5)

vi’ = vi (1 +αi xi )

Where αi can be viewed as a relative

measure of how much one must alter vi to

alter the perceptual quality of the host.

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Determination of α (3/5)

• A large αi means that one can perceptually

‘get away’ with altering vi by a large factor

without degrading the host.

• (2) can be viewed as a special case of the generalized (1) ( vi’ = vi (1 +αi xi ) ), for αi = α vi

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Determination of α (4/5)

A large value is less sensitive to additive

alternations than a small value

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Determination of α (5/5)

• QuestionHow sensitive a image to various additive alternations is highly related to Human perceptual systems.

– Subjective distance measurement : JND– Objective distance measurement

mean αj ~ median | vj

* - vj |

max

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Choice of n (1/2)

• The choice of n indicated the degree to which the watermark is spread out among the relevant components of the image.

• In general, as the number of altered components are increased the extent to which they must be altered decreases - the ability to against attacks ↓

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Choice of n (2/2)

• Information theoretic assessment

– Channel Capacity– Rate-Distortion Theory

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Evaluating the Similarity : Detection scheme

• X* : the extracted mark

• Sim ( X , X* ) = ( X*‧X ) / sqrt ( X*‧X* )• X and X* match, if Sim ( X , X* ) > T• Minimize the rate of

– False Negatives (missed detections)– False Positive (false alarms)

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Other Issues

• Is there any post processing that can improve the watermark detection performance?

– Statistical approach– Optimization approach: NN, GA– With the aid of the knowledge of the host

: Communication with side information– Game Theory

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Attacks (1/2)

• Collision attack

• Image Scaling

• JPEG Compression

• Dithering Distortion

• Cropping attack

• Print, Xerox, and Scan

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Attacks (2/2)

• Attack by Watermarking Watermarked Image- multiple watermarking

Sometimes, this is a must

Sometimes, this produces ‘Watermark Dilemma’


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