Content-based image copy detection using dual signatures

We are interested in content-based copy detection of images as a means for protecting intellectual property. The proposed methodology makes use of the discrete cosine transform (DCT) of an averaged image to extract two complementary features, namely ordinal measures and sign information, yielding a dual signature, i.e., a compact feature vector ensuring efficient storage in the image database. Moreover, a specific similarity measurement scheme is designed to handle dual signature comparison during the image retrieval process. Simulation results show the proposed method to outperform two known copy detection methods in terms of retrieval accuracy. Many common image manipulations can be handled such as noise addition, image resizing, Gamma and contrast adjustment, slight shifting, image flipping and 180° rotation. Achieved retrieval rates are very high and confirm the superiority of the proposed scheme.