Image forgery detection by using No-Reference quality metrics

In this paper a methodology for digital image forgery detection by means of an unconventional use of image quality assessment is addressed. In particular, the presence of differences in quality degradations impairing the images is adopted to reveal the mixture of different source patches. The ratio behind this work is in the hypothesis that any image may be affected by artifacts, visible or not, caused by the processing steps: acquisition (i.e., lens distortion, acquisition sensors imperfections, analog to digital conversion, single sensor to color pattern interpolation), processing (i.e., quantization, storing, jpeg compression, sharpening, deblurring, enhancement), and rendering (i.e., image decoding, color/size adjustment). These defects are generally spatially localized and their strength strictly depends on the content. For these reasons they can be considered as a fingerprint of each digital image. The proposed approach relies on a combination of image quality assessment systems. The adopted no-reference metric does not require any information about the original image, thus allowing an efficient and stand-alone blind system for image forgery detection. The experimental results show the effectiveness of the proposed scheme.

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