Digital Image Watermarking ELE 488 Final Project, Fall 2011 Princeton University Ali JavadiAbhari

Watermarking Why? Fingerprinting (tracking) Indexing (search engines) Copyright protection and owner identification Data hiding Data authentication (fragile watermarks) Recovery (self-embedding) Properties Visibility (transparency) Robustness Payload Security (not through obscurity) 2

In This Project Three techniques 1.Yeung-Mintzer (bit-plane slicing) 2.Cox (spread spectrum watermarking) 3.Fridrich (Self-embedding) They show a wide range of applications of watermarking. Their comparison reveals valuable information. 3

Yeung-Mintzer LUT based fragile watermarking method in the spatial domain Good for tamper detection at single pixel level Limit run of 1s and 0s in LUT to avoid large effect on original image On average only half the pixels are modified 4

Yeung-Mintzer Change to the image are invisible 5

Yeung-Mintzer Change to the image are invisible 6

Yeung-Mintzer Good for local modification detection (e.g. Cropping) 7

Yeung-Mintzer Good for local modification detection (e.g. Cropping) 8

Yeung-Mintzer Very fragile. Lighting up the whole image by 10 intensity values: 9

Yeung-Mintzer Very fragile. Lighting up the whole image by 10 intensity values: 10

Cox Watermarking in the frequency (DCT) domain Series of random numbers ω i are inserted into the K most significant coefficients by: c i '=c i.(1+αω i ) 1≤i≤K Gamma (γ) is a measure to let us know the extent of manipulation, and cannot show where the tampering has taken place (unlike Yeung-Mintzer) and obviously cannot provide the correct form (unlike Fridrich) Quite robust (retains random numbers’ values) 11

Cox The watermark’s effect can be found only with close inspection, still pretty invisible: 12

Cox The watermark’s effect can be found only with close inspection, still pretty invisible: 13

Fridrich Self-embedding DCT of 8x8 blocks is taken, encoded into 64-bit string, and embedded in LSB of destination block Source and destination block linked via an encoding vector, usually 0.3 of image size in length Has recovering capabilities, unlike previous methods The recovered parts are of low quality (less than 50% JPEG) Works best for local changes (forgery) since both destination and source must not be modified 14

Fridrich Example 1: (Vector=0.4hor+0.1ver) 15

Fridrich Example 1: (Vector=0.4hor+0.1ver) 16

Fridrich Example 2: (Vector=0.4hor+0.1ver) 17

Fridrich Example 2: (Vector=0.4hor+0.1ver) 18

Contribution Previous method shows a flaw in the method proposed in the paper by Fridrich If Block 2 is inconsistent with both Block 1 and Block 3, it might be that Block 2 is not tampered but the other two are. Need to check Block 0 to be sure 19

Contribution Example 2: (Vector=0.4hor+0.1ver) 20

Comparisons Yeung-MintzerCoxFridrich Philosophy Detect any change, at the level of individual pixels Be more robust; determine weather change is strong enough or not Detect local tampering, and also correct it Visibility (qualitative transparency) Invisible Effect on original image * (quantitative transparency) % % % Robustness Very FragileVery robustRobust to local modifications Information needed for decoding LUTK largest coefficients, their indices, the random numbers and alpha Encoding vector Recoverable No Yes *Based on Lenna. Measuring average difference between pixels by summing differences, and dividing by image size times pixel range (256) 21

Comparisons Yeung-Mintzer (recovered logo) Cox ( γ ) Fridrich (recovered image) CropShows crop location (unacceptable) Recovered FilterEdges ruined (acceptable) Destroyed IlluminateDestroyed (acceptable) Partial destruction NoiseNoisy (acceptable) Partial destruction RotateSame rotation (unacceptable) - Using morphological transformations as benchmarks: 22

Cropping out a rectangle γ=0.2065(Cox) (Yeung-Mintzer) (Fridrich) 23

Lowpass Filtering γ=0.8856(Cox) (Yeung-Mintzer) (Fridrich) 24

Illuminating γ=0.4870(Cox) (Yeung-Mintzer) (Fridrich) 25

Adding Noise γ=0.5690(Cox) (Yeung-Mintzer) (Fridrich) 26

Rotating γ=0.0170(Cox) (Yeung-Mintzer) (Fridrich) MATLAB out-of-bound access in Fridrich algorithm 27

References [1] M. Yeung and F. Mintzer. Invisible Watermarking for Image Verification. Journal of Electronic Imaging, pp , [2] I. Cox, J. Kilian, T. Leighton, T. Shamoon. Secure Spread Spectrum Watermarking for Multimedia, IEEE Transaction on Image Processing, vol.6, no.12, pp , [3] J. Fridrich, M. Goljan, Protection of Digital Images Using Self- Embedding, Symposium on Content Security and Data Hiding in Digital Media, NJ, USA, May 14, [4] R. C. Gonzalez and R.E. Woods, Digital Image Processing 4th Edition, Prentice Hall, New Jersey, [5] B.B. Zhu, M.D. Swanson and A.H. Tewfik. When seeing isn’t believing. IEEE Signal Processing Magazine, pp. 40–49,

Thank You! Any Questions? 29