By Roland Piquepaille
In our world of digital information, everything is described by zeros and ones, even photographs. And it's incredibly easy to use computers to alter an image after it is digitized to produce fake ones, like the recent forged image showing Jane Fonda and John Kerry together at a political meeting. Now, computer scientists from Dartmouth College have developed an algorithm able to tell the difference between a "real" image and a modified one. They "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." The team thinks that their technology, or a similar one, will soon be incorporated in the U.S. legal system to authenticate images.Here is a description of the problem.
A digital image is a collection of pixels or dots, and each pixel contains numbers that correspond to a color or brightness value. When marrying two images to make one convincing composite, you have to alter pixels. They have to be stretched, shaded, twisted, and otherwise changed. The end result is, more often than not, a realistic, believable image.
"With today's technology, it's not easy to look at an image these days and decide if it's real or not," says Farid, an Associate Professor of Computer Science. "We look, however, at the underlying code of the image for clues of tampering."
And here is brief description of the solution devised by Farid and graduate student Alin Popescu.
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."
Here are two links to the abstract and the full text (PDF format, 15 pages, 4.58 MB) of "Statistical Tools for Digital Forensics," a paper presented at the 6th International Workshop on Information Hiding, held in Toronto, Canada, in 2004.
Here is the text of the abstract.
A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of digital watermarks or signatures. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.
Below is an example of a forgery. The images are extracted from the paper mentioned above (Credit: Hany Farid and Alin Popescu, Dartmouth College).
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Original image |
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The forgery consists of removing a stool and splicing in a new floor taken from another image (not shown here) of the same room. |
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And here is the estimated probability map (p) of the forgery. |
And here is Farid's conclusion.
"This technology to manipulate and change digital media is developing at an incredible rate," says Farid. "But our ability to contend with its ramifications is still in the Dark Ages. I'm always asked if this technology would stand up in a court of law." He explains that the simple answer is, "eventually." Farid predicts there will be skepticism and a great deal of scientific and legal debate. But eventually, he believes that some form of his technology or someone else's will be incorporated into our legal system.
If you are interested or intrigued by this subject, here are two other links to the abstract and the full text (PDF format, 11 pages, 6.50 MB), of another paper, "Exposing Digital Forgeries by Detecting Traces of Re-sampling" (currently available in the IEEE Transactions on Signal Processing).
Sources: Dartmouth College Press Release, July 1, 2004; and various pages at Dartmouth College
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