Applications Classification Common watermarking methods Types of verification/detection Implementing watermarking using
wavelets
Overview
Applications Copyright Protection
◦ Invisibly mark products Manage distribution of assets
◦ Apply unique watermark key to each copy of a distributed video/image
Embed all necessary data in a single image Naturally expands to video watermarking
Simple◦ Spatial Domain – Modification made to the luminance
values Transformed Domain
◦ DCT◦ DWT◦ SVD
Product of 3 matrices A = UΣVT
U ,V are orthogonal matrices: UTU= I, VTV = I Σ = diag (λ1, λ2, ...). The diagonals of Σ are called the singular values of A The columns of U are called the left singular vectors of A and The columns of V are called the right singular vectors of A.
Common Watermarking Methods
An effective watermark should be:◦ Robust to common manipulations◦ Unobtrusive so that it does not affect visual
quality Categorize based on:
◦ Capacity◦ Complexity◦ Invertibility◦ Robustness◦ Security◦ Transparency◦ Verification
Classification
Fragile◦ Detection fails with even minor modification◦ Useful in tampering detection◦ Common in simple additive watermarking
Robust◦ Detection is accurate even under modification◦ Need for robustness dependent on use of data
Robustness
Verification / Detection Methods Non-blind
◦ The watermarking scheme requires the use of the original image
Semi-Blind◦ The watermarking scheme requires the
watermark data and/or the parameters used to embed the data
Blind◦ If the watermarking scheme does not require the
original image or any other data
The 2D-DWT Transform divides the image into 4 sub-bands◦ LL – Lower resolution version of image◦ LH – Horizontal edge data◦ HL – Vertical edge data◦ HH – Diagonal edge data
Most DWT watermarking algorithms embed only in the HL, LH and HH sub-bands
2D – Discrete Wavelet Transform
LL HL
LH HH
◦ Perform 2D-DWT to divide image into LL, HL, LH and HH sub-bands.
◦ Select coefficients from the LL, HL, LH and HH sub-bands that surpass a particular threshold T1
◦ Embed watermarking data via additive modificationt’i = ti + α|ti|xi xi = watermark α = weighting constant
◦ Perform 2D-IDWT to create “watermarked image”
Watermark Embedding Method
Modifications to edge data create the least visually perceptible changes
If using a hard threshold to select coefficients, the number of affected coefficients can vary greatly
Images with a greater number of edges will hold more watermarking data
Watermark Embedding Method (Cont)
Difference
Difference
Method◦ Perform 2D-DWT to divide image into LL, HL, LH
and HH sub-bands.◦ Select coefficients from each sub-band that
surpass a threshold T2>T1.◦ Compute the correlation z, between the
coefficients of the received image (ti*) > T2 and a
particular watermark (yi ).
Watermark Detection Scheme
Compute the threshold Tz. Detection Occurs when z>Tz. Comparison versus other incorrect
watermarks show that the correct watermark is the only one that surpasses the threshold
Watermark Detection Scheme (cont)
0 50 100 150 200 250-5
0
5
10
15
20
Threshold
Watermarks
DWT Watermarking schemes work well against most forms of image modification◦ Jpeg Compression◦ Downsampling -> Upsampling◦ Gaussian Noise◦ Median Filtering
Technique does not work well in cases of image rotation
Dependent on pixel location
Robustness of Scheme
Robustness of SchemeWatermarked - Med Filt Applied
0 50 100 150 200 250-20
0
20
40
60
80Median Filter Applied - T1 = 15, alpha = 0.4
0 50 100 150 200 250-20
0
20
40
60Gaussian Noise Applied - T1 = 15, alpha = 0.4
WatermarkedWatermarked
0 50 100 150 200 250-2
0
2
4
6Horizontally Flipped Image - T1 = 15, alpha = 0.4