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Originally posted by www.dartmouth.edu...
Farid's algorithm looks for the evidence inevitably left behind after image tinkering. Statistical clues lurk in all digital images, and the ones that have been tampered with contain altered statistics.
"Natural digital photographs aren't random," he says. "In the same way that placing a monkey in front of a typewriter is unlikely to produce a play by Shakespeare, a random set of pixels thrown on a page is unlikely to yield a natural image. It means that there are underlying statistics and regularities in naturally occurring images."
Farid's algorithm looks for the evidence inevitably left behind after image tinkering. Statistical clues lurk in all digital images, and the ones that have been tampered with contain altered statistics.
Farid and his students have built a statistical model that captures the mathematical regularities inherent in natural images. Because these statistics fundamentally change when images are altered, the model can be used to detect digital tampering.
Originally posted by jra
Well i just did some more reading on this stuff... apparently this algorithm doesn't work well on lossy formats such as .jpg. Seeing as how pretty much all photos on the net are .jpg or .gif, this makes it rather usless.
There is a range of re-sampling rates that will not introduce
periodic correlations. For example, consider down-sampling
by a factor of two (for simplicity, consider the case where
there is no interpolation). The re-sampling matrix, in this case,
is given by:
(fig 14)
Notice that no row can be written as a linear combination
of the neighboring rows - in this case, re-sampling is not
detectable. More generally, the detectability of any re-sampling
can be determined by generating the re-sampling matrix and
determining whether any rows can be expressed as a linear
combination of their neighboring rows - a simple empirical
algorithm is described in Section III-A.