Image tamper detection based on noise estimation and lacunarity texture

Aiming at the problem of image tampering, a novel detection method is proposed based on the image noise and lacunarity. As there exist differences in image sensor pattern noise and image lacunarity between real image and tampered image, standard deviation of noise, relative frequency lacunarity (RFL), relative frequency mean (RFM) and relative frequency variance (RFV) are extracted from the suspected image to construct feature space. By using LIBSVM classifier, the image is detected if it is tampered or not. Experimental results and analysis show that it can effectively be used for the detection of real image and tampered image, natural image and computer generated graphics. Furthermore, it can be implemented for the detection of artificial blurring in the image with high precision.

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