Wavelet Image Watermark robust against geometric transformations
AbstractTo solve the sensitive problem of signal processing and geometric distortion of digital image watermarking, an image watermarking algorithm against geometric attacks was proposed in the paper. After decomposing the whole image with 3 level of discrete wavelet transform and transforming the watermark image by Arnold shuffling, embed the watermark data to the media frequency coefficients of wavelet domain according to the conceal quality of Human Visual System (HVS); and extract two invariant centroids as factors to correcting geometric transformation by using the theories of invariant centroid, the watermarked image could be corrected. The experimental results show that the algorithm is robust to general signal processing and geometric attack such as rotation, scaling and translation.
Watermark Embedding Challenges
General digital watermark life-cycle phases with embedding-, attacking-, and detection and retrieval functions
SignalsS
Embedding Function E
Attacking Function A
Detecting Retrieval Function
ResultSecure Part In Secure Part In Secure Part
Arnold Scrambling of the Watermark Image
• The transformation for a square digital image is
Wavelet Transformation of a Carrier Image
Watermark Embedding Algorithm
(1) Use Haar wavelet, the images A be done 3 level discrete wavelet transform, to produce LL3, HL3, LH3, HH3 and so on ten sub-band.(2) Use ascending order for the intermediate region HL3, LH3 of image, get the sequence C, and note the location corresponding to order (3) Arnold scrambling the watermark information, then obtain scrambling watermark information W.(4) Using the multiplicative rule, large absolute value coefficient with C embed , then get the watermark information W.c i ′ = ci(1 + alpha*wi)where the size of determines the intensity of the image frequency modified by the watermark signal.
(5) According to corresponding sequence in step (2), the modified media frequency sequence c′i is assigned to corresponding location of original intermediate frequency regions HL3, LH3.
(6) Use the modified wavelet coefficients in step (5) by discrete inverse wavelet transform to get image embedded with the watermark information.
(7) Extract the two invariant centroid points tm, tn of the images embedded watermark information, and obtain the coordinates and corresponding radius r1, r2 of the two points as geometricdistortion correction key for watermark detection.
Watermark Detection Algorithm
(1) Use the methods described before as well as the key of geometric distortion of thewatermark image rotation, scaling, translation correction.
(2) Use DWT for watermarking image A ∗ with geometric distortion correction to get LL3, HL3,LH3, HH3 and so on ten sub-bands.
(3) According to the corresponding position sequence and the embedded watermarksequence size, a embedding position of intermediate frequency regions HL3, LH3 in watermarkimage is determined, and embedded watermark sequence c i ′ is obtained.
(4) Use the Eq. (4), to get scrambling watermark information W′.W′i = (c /ci − ′ 1)/alpha
(5) Use the saved Arnold scrambling key to do periodic transformation for W′, then get theextracted watermark image W∗.
Extraction of the Invariant Centroid and Parameter Correction of the
Geometric Distortion
• Extraction of the Invariant Centroid• Parameter Correction of Geometric Distortion• Image Rotation Correction Algorithm• Image Scaling Correction Algorithm• Image Translation Correction Algorithm
Wavelet Hiding strategy
Host Image Wavelet transform
LL3,LH3,HL3
Use LL3 but low energy or
LH3 and/or HL3 for higher
energy
Another strategy use only high value coefficients to hide your coefficients
Another strategy use additive way or multiplicative way
Watermark Algorithm
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use LL3 but low energy
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
Arnold scrambling
Original image and its wavelet transform
Watermark and its wavelet transform
Watermarked Image
Scrambled and Descrambled Watermark
USE HL Band to Hide
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
Arnold scrambling
Why 3 level wavelet transform
Because it is more robust to attacks
Image Rotation Attack with correctionin CH Band(HL)
ans = Correlation between watermark and recovered watermark 0.1215
RC4 could be used instead of Arnold
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
RC4 EncryptionSecret Key
Other permutation methods can be used instead of Arnold
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
Permutation
Encrypt Watermark using XOR
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
XORSecret key
Increase the level of wavelet
Host Image Wavelet transform
Wavelet transform
Wavelet transform
LL4,LH4,HL4Use HL4 or LH4
or LL
Watermark Image
+*Wavelet inverse 3 levels
Watermarked Image
XORSecret key
Wavelet transform
Generate pseudo random signals for fingerprinting
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
+*Wavelet inverse 3 levels
Watermarked Image
Pseudo random
generator or LFSR
Hide Watermark inside another Watermark
Host ImageWavelet
transformWavelet
transformWavelet
transform
LL3,LH3,HL3Use HL3
+*Wavelet inverse 3 levels
Watermarked Image
Arnold Scrambling
Host Watermark
Watermark #2
Watermarked Watermark