Most digital forgeries use an interpolation function, affecting the underlying statistical distribution of the image pixel values, that when detected, can be used as evidence of tampering. This paper provides a comparison of interpolation techniques, similar to Lehmann [1], using analyses of the Fourier transform of the image signal, and a quantitative assessment of the interpolation quality after applying selected interpolation functions, alongside an appraisal of computational performance using runtime measurements. A novel algorithm is proposed for detecting locally tampered regions, taking the averaged discrete Fourier transform of the zero-crossing of the second difference of the resampled signal (ADZ). The algorithm was contrasted using precision, recall and specificity metrics against those found in the literature, with comparable results. The interpolation comparison results were similar to that of [1]. The results of the detection algorithm showed that it performed well for determining authentic images, and better than previously proposed algorithms for determining tampered regions.
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