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

Thanks!