multiple watermarking
DESCRIPTION
Multiple watermarking. Wu Dan 2007.10.10. Introduction (I). Multipurpose watermarking Ownership watermarks (very robust) Captioning watermarks ( robust) Verification watermarks( fragile) Multi-user watermarking The difficulty of multiple watermarking is the order. - PowerPoint PPT PresentationTRANSCRIPT
Multiple watermarking
Wu Dan2007.10.10
Introduction (I)
Multipurpose watermarking Ownership watermarks (very robust) Captioning watermarks ( robust) Verification watermarks( fragile)
Multi-user watermarking The difficulty of multiple
watermarking is the order.
Introduction (II)
The basic method of watermarking SS (spread spectrum) x’=x+αw QIM (quantization index module)
Odd even odd even
0 1 0 1
Multipurpose Watermarking for Image Authentication and Protection
Chun-Shien Lu, Member, IEEE, Hong-Yuan Mark Liao, Member, IEEE
IEEE TRANSACTIONS ON IMAGE PROCESSING,
OCTOBER 2001
I ) cocktail watermarking scheme Bipolar watermarking Complementary modulation Use of a wavelet-based human
visual system to control the hiding strength
II) Proposed multipurposealgorithm
Wavelet transform
Quantization of wavelet coefficient
S: scale o: orientation (x,y): position
MTU: masking threshold units
Negative modulation
Positive modulation
q(|p(x,y)|) is regarded as the embedded watermark values.
Negative modulation
positive modulation
Host image recovery The difference between a recovered
wavelet coefficient and its corresponding original wavelet coefficient
Watermark detection
Compare the hidden watermark (K) and the extracted one ( )
Detection of robust watermark
Detection of fragile watermark
A novel blind multiple watermarking technique for images
Peter H. W. Wong, Member, IEEE, Oscar C. Au, Senior Member, IEEE, and Y. M. Yeung
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, AUGUST 2003
I) SWE (single watermarking embedding)
Select image pixels or transform coefficients
Watermark host vector:
Watermark: A pseudorandom bit sequence:
The first key: a set of N pseudorandom positive real numbers
The second key: being zero-mean Gaussian with variance
Split K and Y into N subvectors of equal length
Force the projection of Yi to be the center of the nearest cell of the desired watermark bits
Decode the watermark of SWE
II) MWE
Embed Q bits simultaneously in each subvector Yi.
The first key: The second key:
Direct approach
Iterative approach Decode and detection
III) IWE in JPEG compressed domain
Problem: when the original image for the
proposed watermarking algorithm is a JPEG-compressed image and the watermarked image needs to be JPEG recompressed to produce another .jpg.
Would the watermark still be decodable?
Watermark host vector: Y1=(f1(0,1),f2(0,1) ,……f32*32(0,1))Y2=(f1(1,0),f2(1,0) ,……f32*32(1,0))…… in zigzag order. Y’i=Yi+Ni
Near optimal watermark estimation and its countermeasure: antidisclosure watermark for multiple watermark embedding
Chun-Shien Lu, Member, IEEE, and Chao-Yung Hsu
2007.4IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECH
NOLOGY,
I) Watermark estimation
X: the original image; : the watermarked image. :the attacked image
Conventional attacks:
The collusion attack
Copy attack: the estimated watermark can be inserted into unwatermarked media data to produce a counterfeit watermark data.
Compare denoising attack and copy attack X: the original image; : the watermarked imag
e. :the estimated watermark
is the watermark extracted from
if BER( , ) >threshold, the denosing attack succeed.
Z: the faked original image; : the faked watermarked image.
is the watermark extracted from
if BER( , ) <threshold, the copy attack succeed.
A smaller threshold resist copy attack.A larger threshold resist denosing attack.
II) Optimal watermark estimation
Necessary Condition for Optimal Watermark Estimation
{ }
Sufficient and Necessary Condition for Optimal Watermark Estimation
Perfect cover data recovery
A near-perfect cover data recovery algorithm
For each embedding unit with index q. We adopt Weiner filtering for denosing purpose to get an estimation .
Collusion Estimation of Watermark Sign:
Estimation of Watermark Magnitude via Visual Model for Complete Removal:the wavelet coefficient for the recovered image is:
III) Content dependent watermark
Media hash (MH)The magnitude relation ship
between two AC coefficient at blocks u and v.
This feature value is verified to be robust because this magnitude relationship can be mostly preserved under incidental modifications (e.g., compressions, filtering, and denoising).
CDW (content-dependent watermark)
Resistant collusion attack
Resistant copy attack
Conclusion
Non-uniform quantization Design the perfect CDW
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