  Detection of inconsistencies of double JPEG artefacts across different image
regions is often used to detect local image manipulations, like image splicing,
and to localize them. In this paper, we move one step further, proposing an
end-to-end system that, in addition to detecting and localizing spliced
regions, can also distinguish regions coming from different donor images. We
assume that both the spliced regions and the background image have undergone a
double JPEG compression, and use a local estimate of the primary quantization
matrix to distinguish between spliced regions taken from different sources. To
do so, we cluster the image blocks according to the estimated primary
quantization matrix and refine the result by means of morphological
reconstruction. The proposed method can work in a wide variety of settings
including aligned and non-aligned double JPEG compression, and regardless of
whether the second compression is stronger or weaker than the first one. We
validated the proposed approach by means of extensive experiments showing its
superior performance with respect to baseline methods working in similar
conditions.
