Image adaptive RST invariant watermark using pseudo-Zernike moments

Abstract

Rotation, scaling and translation (RST) attacks can desynchronize watermark detection, which causes failure in many watermarking systems. In this paper, an image adaptive RST invariant watermark (AWPZM) is proposed by using the rotation invariant property of pseudo-Zernike moments (PZM) and odd-even quantization. PZM of the original image is computed first, and then those suitable for watermark generation are selected. Then, magnitudes of them are odd-even quantized to generate the watermark. In detection, a normalized hamming function is employed to determine the similarity of the watermark. Experimental results show its robustness to rotation and scaling. For traditional attacks, such as JPEG compression, added noise and filtering, the similarities are all above 0.95.

References

1. Barnett R Digitalwatermarking: applications, techniques and challengesElectronic & Communication Engineering Journal 1999 11(4)173183
2. Kim H S Lee H K Invariant image watermark usingZernike momentsIEEE Transactions on Circuitsand Systems for Video Technology 2003 13(8)766775
3. Chen Q Yang X L Zhao J Y Robust image watermarking with Zernike momentsProceedings of Canadian Conference on Electricaland Computer Engineering 2005 13401343
4. Xin Y Q Liao S Pawlak M A multibit geometrically robust image watermark based onZernike momentsProceedings of 17th InternationalConference of Pattern Recognition 2004 4: 861864
5. Kundur D Hatzinakos D Digital watermarking for telltaletamper proofing and authenticationProceedingsof the IEEE 1999 87(7)11671180
6. Guo L Guo B L Chen L T et al.Wavelet zerotree-based watermarking algorithms forstill imagesJournal of Xidian University 2003 30(5)677681 (in Chinese)
7. Guo B L Zhang Y P Guo L Hierarchical semi-fragile watermarking based on the reconstructionof DCT coefficientsJournal of Xidian University 2004 31(1)15 (in Chinese)
8. Zhang Y P Guo B L SAF: semi-fragile authenticationwatermarking with feedbackJournal of XidianUniversity 2004 31(5)724727 (in Chinese)
9. Teh C H Chin R T On image analysis by the methodsof momentsIEEE Transactions on PatternAnalysis and Machine Intelligence 1988 10(4)496513
10. Liao S Pawlak M On the accuracy of Zernikemoments for image analysisIEEE Transactionson Pattern Analysis and Machine Intelligence 1998 20(12)13581364