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Automatic noise-based detection of splicing in digital images

Abstract : In this dissertation, we offer three new forensics imagery methods to detect splicing in digital images by exploiting image noise statistics. To do so, we introduce a new tool, the noise density histogram, and its derivative, the noise density contribution histogram. Our methods allow splicing detection on both raw and JPEG images. Although the use of noise discrepancies to detect splicing has already been done multiple times, most existing methods tend to perform poorly on the current generation of high quality images, with high resolution and low noise. The effectiveness of our approaches are demonstrated over a large set of such images, with randomly-generated splicings. We also present a detailed analysis of the evolution of the noise in a digital camera, and how it affects various existing forensics approaches. In a final part, we use the tool we developed in a counter-forensics approach, in order to hide the trace left by splicing on the image noise
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Thibault Julliand. Automatic noise-based detection of splicing in digital images. Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Est, 2018. English. ⟨NNT : 2018PESC2057⟩. ⟨tel-02476516⟩

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