Rotation, Scale and Translation Resilient Public Watermarking for Images

Transcription

1 Roaion Scale and Translaion Resilien Public Waermarking or Images Ching-Yung Lin a Min Wu b Jerey A. Bloom c Ma L. Miller c Ingemar J. Cox c Yui Man Lui d a Columbia Universiy New York NY b Princeon Universiy Princeon NJ c NEC Research Insiue Princeon NJ d Signay Inc. Princeon NJ January 4 000

2 Moivaion: Prin-and-Scan Process original prining prined documen scanning rescanned image Pixel Value Disorion Geomeric Disorion: -- Roaion Scale -- Translaion Crop Change o Boundary Padding

3 Public Waermarking surviving Geomeric Disorion Ideniicaion Inormaion N E C I Original Image Characerisics: Invisible Robus and Blind Previous Work: Waermarked Image nd waermark (sel-regisraion emplae: Univ. o Geneva 998. Recognizable srucure: Kuer 998. Invarian coeiciens: O Ruanaidh 998.

4 Ouline Proposal: Embedding waermarks by shaping an invarian one-dimensional eaure vecor derived rom he log-polar map o DFT o image. Implemenaion Diiculies and Soluions: Experimens: False posiive ess or 0000 images; Robusness ess or 000 images. Conclusion and Fuure Work

5 Discree Fourier coeiciens o discree images aer RST Roaion in spaial domain => Roaion in requency domain Scaling wihou change o boundary in spaial domain => Scaling in requency domain Translaion in spaial domain => Phase shi in requency domain Scaling wih boundary change Cropping in spaial domain => Noise in requency domain ( cos sin sin cos ( cos sin sin cos ( ( X X x x R F R = + + = ( ( ( ( X X x x S F S = = λ λ λ λ

6 The Log-Polar Map o Fourier Coeiciens Log-Polar Map log r For RST (uniorm scaling Roaion: shi in he axis Scale: shi in he log r axis. Translaion: no eec on he magniudes. Projecion along he log r axis: Cyclic shi or roaion Invarian o scaling.

7 Waermark Embedding: Feaure Vecor Shaping Spread Specrum: ( F w = ( F + W Feaure Vecor Shaping ( F w W eaure vecor waermark vecor modiied eaure vecor (mixed signal Exrac a Noise-Like Feaure Vecor and change i o a waermark paern

8 Diiculies and Soluions Log-Polar Map o Fourier Coeiciens: Soluions => Zero-Padding bilinear inerpolaion rom he magniudes o DFT coeiciens. Inverse Log-Polar Map (-o-many many-o- mapping: Soluions => Esimae changes rom he log-polar map and ieraive embedding on he DFT coeiciens Noise-Like Feaure Vecor: Soluions => Local Variance Whiening iler Summing logs Summing g(+g(+90 Visual Qualiy: Soluions => Consrain on he DFT Coeicien Varianions

9 DFT coeiciens aer roaion Original Image Aer Roaion Aer Roaion and Cropping Specrum Characerisics: cross eec Caresian sampling poins => Soluions: Esimae he cross posiions rom boundary/ larger values

10 Algorihm or generaing eaure vecor Zero-padded o double image size Calculae he magniudes o log-polar coeiciens Fm rom DFT magniudes Summaion o he log o he Fourier- Mellin magniudes along log r axis Combine values in orhogonal direcions g ( = g 0 ( + g 0 (+90 Subrac g( by is global mean (whiening iler The Feaure Vecor v = g( l u eaure vecor waermark vecor modiied eaure vecor

11 Experimens: Prin-and-Scan Original Image [384x56] Waermarked Image PSNR 43.8dB ρ=0.84 Z=7.0 Aer Prin & Scan Crop o 40x66 => ρ=0.80 Z=6.46 Aer PS Crop o 360x40 & JPEG CR: 95: => ρ=0.64 Z=4.30

12 Experimens: False Posiive (0000 images rom Corel Image Library 0 dieren waermarks

13 Experimens: Robusness (ROC curves o 000 images Roaion ( Trans- Laion (5% 0% 5% 0% Scale down (5% 0% 5% 0% Scale Up (5% 0% 5% 30%

14 Experimens: Robusness (ROC using 000 images JPEG Compression (

15 Conclusion: Conclusion and Fuure Work The waermarking mehod: Uilize a signal ha changes in a rivial manner as a resul o RST. Feaure Vecor Shaping: Embedding One-dimensional Waermark. Relaed Work: Disorion Modeling and Invarian Exracion or Digial Image Prin-and-Scan Process ISMIP Taipei Dec 999. Fuure Work: Exensive es on he prin-and-scan images. Enhance he robusness o he sysem in embedding muliplebis waermark and cropping