PROPOSAL “EDGE STEGO DIGIT WATERMARK” A. Astapkovich State University of Aerospace...
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Transcript of PROPOSAL “EDGE STEGO DIGIT WATERMARK” A. Astapkovich State University of Aerospace...
PROPOSAL “EDGE STEGO DIGIT WATERMARK”
A. Astapkovich
State University of Aerospace Instrumentation
2011
Preliminary_1: digital fingerprinting formalizm• Special notations will be used:
• In common case watermarking suppose using the encryptions and whole procedure is described with I x K x M → I”
where I - container imageK – encryption keyM – watermark
• Attack is described with
I” x A → IA” container image after attack MA” watermark image extracted from attacked image
• Number of pixel in image Im will be described as
NP (Im)
Preliminary_2: robustness measurements
Let us N (I1,I2) is some norm for difference between images I1 and I2
Robustness is described with the vector
N ( I, I”) - quality of the watermarked image
N (I, IA”) - attack wildness
N (M,MA”) – quality of the extracted watermark image after the attack
N (M,MA”)/ N (I,IA”) - relative robustness of the watermarking procedure;
Goal of the research
THEORY
Creating the knowledge base to support research activity in digital watermarking and digital fingerprinting fields; Developing and investigating the new concepts for robust
watermarking and fingerprinting algorithm oriented for the digit video applications;
PRACTICE
The digital fingerprinting method for high quality video files has to be developed;
The method has to be realized as the demo version of the soft tool for fingerprinting;
Basic requirements - practice
Video file has format 1080p60 and is compressed with MPEG4;
1 min segment of video file can be used for the fingerprinting;
Method has to withstand attacks:
rotation, small enlargements with cropping, noising,
small nonlinear distortions, down sampling up to
720p30, collusion attack with at least 100 copies;
Basic requirements - theory
Volume of the container (set of images) is the huge in comparizon with the marking information
NP (M ) << NP( ∑ Im ) (C.1)
Any watermark can be destroyed with severe attack and to provide the surviving of the fingerprint method has to meet the condition
N (M,MA”)/ N (I,IA”) < 1 (C.2) Existence of the embedded watermark is more important than the quality of
extracted image, but has to be good enough to be recognized
N (M,MA”) < prescribed level (C.3)
Edge watermark concept
Any watermark can be destroyed by attack with strong enough wildness
Attacked image has to have
N (I, IA”) < corrupted quality
Watermarking method has to be build such way that
N (M,MA”)/ N (I,IA”) < 1 It is reasonable to build the watermark to most fragile element of image, like edges
Possibility to use edge region has to be investigated, also
Digital Fingerprinting Method (DFM)
DFM includes:
method to Generate the Fingerprint Set (GFS)
method to generate the Set of Marking Positions (SMP)
method to Embed the Marking Information at selected marking positions (EmMI) and method to Extract the Marking Information (ExMI)
method to Interpretation Extracted Fingerprint (IEF)
Generating the Fingerprint Set (GFS)
To provide robustness the fingerprinting information is converting to set of the images
C 0 1 2 3
C0123… -> I0, I1, I2,I3 ,I4..
This images have to be embed to appropriate frames of video to survive against conclusion attack
Images can be used directly or as wavelet decomposition components: LL,LH,HL,HH and so on
Generating the Set of Marking Positions (SMP)
To provide robustness against collusion attack SMP has to provide possibility the Boneh-Shaw fingerprint scheme (code)
For c+1 total users Boney-Shaw code uses
O ( с3 log (1/ε) )
bits to attain security against coalition of size c with error ε
Boneh-Shaw code is used as building block for many sophisticated digital watermarking schemes
Condition (C.1 ) provides possibility to build method with Boneh-Shaw code for C >> 100
Distortion against collusion
Other possibilities have to be proposed and investigated As example :
Collision attack based on difference of the images and creating the new version to eliminate the distributor ability to trace the object to any of them
Little distortion of images with digital fingerprint destroy the simple collusion attack scheme
Simple averaging will destroy the images and make collusion copy worthless
Example of the edge watermarking
(I).bmp = 640x480
NP (EDGE) = 68698 NP(M) = NP( 100x100)= 104 бит
(I x M ).bmp = 640x480
• Marking Image was build to set of G and B components
of edge pixels, generated with CANNY edge detector;
Extracted M
Method to Embed the Marking Information (EmMI)
Kutter algorithm for embedding provides defense against the noising attack
Mi - bit of marking information I = {R,G,B} container
p(x,y) – selected position for embedding ;
Mi is embedded to B channel
L(p)= 0.299 R(p) +0.587 G(p)+0.114 B(p) B(p) + q*L(p), if Mi =0
B(p)” = B(p) - q*L(p), if Mi =1
q – robustness parameter ( larger q leads to better robustness)
In order to increase robustness every bit is embedded r times as the cross figure (7*7, с = 3), so total number is
N = 3*r ;
Method to Extract the Marking Information (ExMI) ;
Kutter algorithm for blind ExMI related with EmMI
The bit value is determined by looking at the sign of the
difference between the pixel under inspection and the estimated original
Modification of ExMI + EmMI on base Kutter algorithm for
edge pixel set has to be developed
Robustness of the wavelet decomposition
As estimates the result of work Mohsen Ashourian, Peyman Moallem, Yo-Sung Ho “A Robust Method for Data Hiding in Color Images” can be used //PCM (2) 258-269 , 2005;
LL,HL,LH,HH Haar components of watermark (MASTER b/w
image) is imbedded with modified Kutter algorithm
a b
c d(a+b+c+d)/4
(a-b+c-d)/4
(a+b-c-d)/4 (a-b-c+d)/4
LL LH
HL HH
MH1MASTER_PIC
Eхамples of the wavelet decomposition approach
LL LH
HL HH
• Watermark MASTER image had the size ½ * ½ * 1/3 of the container image;
Container images
Robustness against the compression attack
PSNR for compression with JPEG
• The published results demonstrate the good robustness properties against compression with JPEG and JPEG2000
Extracted watermark image
for Q=55
• Robustness to MPEG4 has to be investigated ;
Robustness against down sampling attack
Origin watermark Extracted watermark
after ½ down sampling
• This is possible due to decomposition of the watermark image and the mixing of Haar components during embedding
Robustness against the filtering attack
PSNR (dB) for extracted M MF GF
Parrots 20.65 25.80Boats 21.65 24.43
Median filtering (MF) Gauss filtering (GF)
Robustness against the cropping attack
IA” MA” for 10% cutting
• Wavelet decomposition of the M provides high robustness for this type of attack ;
Open questions
Robustness is described with the vector and
some approaches have to be investigated:
what norm has to be used ?
is it the same norm has to be used for all components ?
what level of norm meanings has to be defined from practical point of view ?
For IEF stage norm has to be selected also ;
Brief norm review Classical approach is using the peak signal-to-noise ratio (PSNR) and MSE ;
MAXI — maximal pixel meaning value (for 8-bit w/b image MAXI = 255);
PSNR is no sign measure and useful for small distortions case;
Original image Enhanced contrast
PSNR=25 dB
JPEG compression
PSNR=25 dB
Attack wildness measurements
MASTER_PIC
To measure the attack wildness the linear measure has to be used (at least)
NL 105 0
Nx 1
i 0
Ny 1
j
O_Ij i D_I
j i 2
Nx Ny MAX
NOISY_MASTER_50
NOISY_MASTER_100 NOISY_MASTER_200
0 50 100 150 2000
10
20
30
40
50
60
PSNRi
i
0 50 100 150 2000
100
200
300
NLi
i
Salt and pepper noise attack Noise amplitude
PSNR can be modified (as example)
Modern structural norms
Original image
MSE=0
SSIM=1
CW-SSIM=1
MSE=306
SSIM=0.928
CW-SSIM=0.938
Enhanced contrast Distorted brightness
MSE=309
SSIM=0.987
CW-SSIM=1
Gauss noise
MSE=309
SSIM=0.576
CW-SSIM=0.814
Zhou Wang and Eero P. SimoncelliTranslation insensitive image similarity in complex wavelet domain / Proc. IEEE Inter. Conf. Acoustic, Speech & Signal Processing Volume II, Pages 573-576, March 2005
Some new ideas based on structural symmetry (SSIM) and complex wavelet structural symmetry (CW-SSIM) can be useful for the watermarking applications
Modern structural norms
MSE=313
SSIM=0.73
CW-SSIM=0.811
Impulse noise JPEG
compression
Enlargement
MSE=309
SSIM=0.58
CW-SSIM=0.63
MSE=694
SSIM=0.505
CW-SSIM=0.925
MSE= 873
SSIM= 0.399
CW-SSIM=0.933
Rotation to left
Metrology benchmark image library and software tools have to be created
Structural similarity norm can be useful for estimation of the quality of
the extracted watermark N (M,MA”)
Noiseproof edge detector
Edge watermarking withstand some attacks like cropping, rotation, displacement easily
For blind watermarking this approach needs a noise proof edge detector
Concept of the neuron like adaptive noise proof edge detector was proposed and investigated
“The teaching by showing” methodology was used : this approach is very flexible : different samples can be used during initial learning filter can increase experience due to additional learning with new samples
This approach can be classified as “open algorithm approach”
Canny edge detector
Clean image
Noisy image edges Noisy image
Canny filter is the best edge detector, but …..
Clean image edges
Neuron adaptive linear filter
Initial learning min II Sw – F II w
Adding the new experience is not very expensive procedure
Let us Sek = ∑ SkTSk - experience matrix for k samples
Fek = ∑ SkTFk – experience vector for k samples
New filter weights Wk+1 = (Sek + S k+1T Sk+1 + E) –1 * (Fek+Sk+1
T Fk+1)
S1 S2 S3
S4 S5 S6
S7 S8 S9image
Filter 3*3S1
S2
SNSEN
1
(S,W)
THmin THmax
W = (ST S + E) –1 ST F
Neuron like adaptive noise proof edge detector
NHL5_FIG_2N
TRmin = 40
NHL5_FIG_1N
TRmin = 40
NHL5_FIG_2C
TRmin = 30
Results of filtering for the clean and noisy test image with H51 and H52 filters with different low threshold (Trmin)
H51
1.431 103
0.069
0.137
0.083
4.21 103
0.073
0.143
0.094
0.164
0.066
0.134
0.09
0.371
0.076
0.151
0.071
0.148
0.091
0.159
0.074
8.796 103
0.071
0.145
0.07
3.713 104
H52
6.134 103
0.051
0.034
0.052
0.012
0.036
0.117
0.044
0.128
0.037
0.051
0.034
0.541
0.036
0.054
0.041
0.107
0.04
0.13
0.041
4.03 103
0.043
0.038
0.047
9.927 103
H51_CONST 4.991H52_CONST 2.265
Methodology is universal and has no limits on size of edge filter
Example of 5*5 filter, learned with one and two sample noised images
MASTER_PIC
M_TEST_52H_30 M_TEST_52H_60 M_TEST_52L_30 M_TEST_52L_60
Real image test
Two linear 5*5 filter were used to find the edges
with different prescribed low thresholds
Artificial generated pictures with added noise were
used to generate the learning samples
Different teacher samples were used
Filter, learned with LAPLAS edge detector
Filter, learned with hand pointed edge
low threshold : 30 and 60
Conclusion : result is the vector
Method component
The fingerprinting based on multiple description subband ( wavelet decomposition) image coding, that is embedded to edge domain with modified Kutter algorithm has to be investigated as the possible solution
Some preliminary estimates demonstrate a good robustness against the various signal processing and geometrical attacks
2D embedding process (spatial + time) domains has to be investigated as the defense against the set of attacks (includes collusion)
Theory component
Vector approach for robustness measurements has to be developed and investigated Adaptive algorithm on base of neuron net approach for edge detector has to be developed and investigated
Practice component
Metrology base has to be created
The demo version of the digital fingerprinting tool has to be created