1. Adam Day

2.  Applications  Classification  Common watermarking methods  Types of verification/detection  Implementing watermarking using wavelets

3.  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

4.  Simple ◦ Spatial Domain – Modification made to the luminance values  Transformed Domain ◦ DCT ◦ DWT ◦ SVD  Product of 3 matrices A = UΣV T  U,V are orthogonal matrices: U T U= I, V T V = I  Σ = diag (λ 1, λ 2,...).  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.

5.  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

6.  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

7.  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

8.  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 LLHL LHHH

10. ◦ 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 modification t’ i = t i + α|t i |x i x i = watermark α = weighting constant ◦ Perform 2D-IDWT to create “watermarked image”

11.  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

13.  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 (t i * ) > T2 and a particular watermark (y i ).

14.  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 Threshold Watermarks

15.  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

17.  DWT-Based watermarking methods are fast /robust and protect against most forms of manipulation  Schemes based on pixel dependency are robust in most forms of image manipulation, but fail when significant pixels are moved from their original location