Abstract
Multipurpose watermarking for content authentication and copyright verification are accomplished by using the multiscale curvelet transform. A curvelet transform gains better and sparser representation than most traditional multiscale transforms. In this paper, an image is decomposed into multiscale coefficients with a dyadic number of wedges constructed from a variety of neighboring scales. Image hash is designed to extract image features from an approximate scale. The image features represented in the form of bit sequences are then embedded onto the wedges by a quantization based on human visual system behavior. The implementation strategy achieves content authentications for fatigue watermarking and copyright verifications for robust watermarking. The experiments demonstrate good results to support the feasibility of using this method in multipurpose applications.
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