But digital technology does provide clues that experts can exploit to identify the fakery. In most cameras, each cell registers just one color-red, green or blue-so the camera´s microprocessor has to estimate the proper color based on the colors of neighboring cells, filling in the blanks through a process called interpolation. Interpolation creates a predictable pattern, a correlation among data points that is potentially recognizable, not by the naked eye but by pattern-recognition software programs.
Farid has developed algorithms that are remarkably adept at recognizing the telltale signs of forgeries. His software scans patterns in a data file´s binary code, looking for the disruptions that indicate that an image has been altered. Farid, who has become the go-to guy in digital forensics, spends a great deal of time using Photoshop to create forgeries and composites and then studying their underlying data. What he´s found is that most manipulations leave a statistical trail.
Consider what happens when you double the size of an image in Photoshop. You start with a 100-by-100-pixel image and enlarge it to 200 by 200. Photoshop must create new pixels to make the image bigger; it does this through interpolation (this is the second interpolation, after the one done by the camera´s processor when the photo was originally shot). Photoshop will "look" at a white pixel and an adjoining black pixel and decide that the best option for the new pixel that´s being inserted between them is gray.
Each type of alteration done in Photoshop or iPhoto creates a specific statistical relic in the file that will show up again and again. Resizing an image, as described above, creates one kind of data pattern. Cutting parts of one picture and placing them into another picture creates another. Rotating a photo leaves a unique footprint, as does "cloning" one part of a picture and reproducing it elsewhere in the image. And computer-generated images, which can look strikingly realistic, have their own statistical patterns that are entirely different from those of images created by a camera. None of these patterns is visible to the naked eye or even easily described, but after studying thousands of manipulated images, Farid and his students have made a Rosetta stone for their recognition, a single software package consisting of algorithms that search for seven types of photo alteration, each with its own data pattern.
If you employed just one of these algorithms, a fake would be relatively easy to miss, says digital-forensic scientist Jessica Fridrich of the State University of New York at Binghamton. But the combination is powerful. "It would be very difficult to have a forgery that gets through all those tests," she says.
The weakness of Farid´s software, though-and it´s a big one-is that it works best with high-quality, uncompressed images. Most nonprofessional cameras output data files known as JPEGs. JPEGs are digitally compressed so that they will
be easy to e-mail and won´t take up too much space on people´s hard drives. But compression, which throws away less-
important image data to reduce size at the expense of visual quality, removes or damages the statistical patterns that Farid´s algorithms seek. So at least for now, until Farid´s next-generation software is finished, his tool is relatively powerless to
provide information about the compressed and lower-quality photos typically found on the Internet.