Memon’s program relies on the fact that digital cameras record image information in discrete squares of color, or pixels. Each pixel consists of a sensor for red, blue, or green light. “You don’t have all three [sensors] at any point,” Memon explains, so cameras use “interpolation” algorithms to adjust the color of an individual pixel based on readings from the surrounding pixels. These algorithms vary from company to company, and they “leave telltale artifacts” on pictures, Memon says. In this way an image from one camera can be distinguished from one taken with another.
So far, Memon and his students in Brooklyn have catalogued the color estimation styles of 10 different manufacturers. Memon notes, however, that there is a difference between each company’s high-end and mid-range models. The technique is about 90 percent accurate, Memon says, but as the number of digital cameras on the market grows, it becomes more difficult to match a picture to a camera brand.
Memon’s technique is useful when investigators are hunting down a camera and a photographer; but in some instances, the camera is already part of the evidence. In these cases, a technique developed by Jessica Fridrich at the State University of New York in Binghamton can help to prove that an individual picture came from a specific camera.
To accomplish this bit of sleuthing, Fridrich exploits the fact that every camera produces tiny imperfections, or “noise,” within an image. “If you zoom in on a portion of a picture that’s supposed to be a uniform blue sky, you’ll see those pixels are not monotonous blue,” she explains. “You’ll start to see irregularities.”
Fridrich’s software extracts these irregularities from a large number of pictures captured by the same camera. (Since investigators have access to the camera, they can take as many pictures as needed.) Because each individual camera has a characteristic way of producing noise, the irregularities can be averaged to create a unique signature, and individual photos can be checked against this signature. The technique is accurate 99.99 percent of the time, according to Fridrich. “We have discovered the equivalent to matching a bullet to the barrel and gun,” she says.
Fridrich’s technique works even after a picture has been compressed to a smaller file size, to be sent in an e-mail, for instance. In contrast, digital forensic techniques like Memon’s fail if a file has been shrunk. “The beauty of [noise correlation] is that it is robust to distortion,” says Fridrich.
Recently, Fridrich has been extending her noise analysis technique to determine if certain regions of an image have been altered. If the noise is not uniform across the picture, a segment has been tampered with, she says.
“Everyday, somewhere in the world, you have someone questioning the veracity of an image,” Memon says. Both Memon’s and Fredrich’s tools are useful in different settings – and together, they should help make it harder for photo forgers to dupe the unwitting public.