Geometrically robust digital image watermarking using scale normalization and flowline curvature

Abstract

The growth of internet communications, multimedia storage capacity, and software sophistication triggered the need to protect intellectual property in digital media. Digital watermark can be inserted into images for copyright protection, copy protection, tamper detection and authentication. Unfortunately, geometrical robustness in digital image watermarking remains a challenging issue because consumer software enables rotational, scaling and translational attacks on the watermark with little image quality degradation. To balance robustness requirements and computation simplicity, we propose a method to re-synchronize watermark information for its effective detection. The method uses scale normalization and flowline curvature in embedding and detection processes. Scale normalization with unit aspect ratio and predefined area offers scale invariance and translation invariance. Rotational robustness is achieved using the flowline curvature properties of extracted robust corners. The watermark is embedded in Discrete Fourier Transform (DFT) domain of the normalized image using fixed strength additive embedding. Geometric properties recovery is simplified using flowline curvature properties and robust corners as reference points prior to watermark detection. Despite the non-blind nature and vulnerability to local transformations of this approach, experimental results indicate its potential application in robust image watermarking.