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Using Noise as Camera Fingerprints for Detecting Image Manipulation



A recent photographic analysis technique developed by Professor Siwei Lyu and his team at the University at Albany – SUNY could lead to better forensic analysis of altered images. The technique takes advantage of the fact that, when splicing two images together, each will bring with it the specific noise pattern of the camera it was shot with.

So, when analyzing the obviously fake image at the top, the flamingo Tiger Woods is using in lieu of his golf club shows up as having a different noise pattern than the rest of the image.

Here’s another example of a spliced image and its noise analysis data:


The system isn’t perfect. For example, extensively smooth or textured regions in an image can impact noise detection, leading to false positives. A skilled image manipulator could also, knowing of this technique, introduce a smooth noise pattern.

But even so, this adds another tool to a growing manipulation detection toolbox for forensic analysis. So forgers beware, you can no longer pass off your “Tiger Woods Using a Flamingo as a Golf Club” photo as the real deal.

For all of the technical minutia and more examples, you can read the entire research paper here.

(via Fourandsix)