An algorithm sniffs out digital alterations
Context: Photographs used to be a reliable source of evidence, but the advent of digital cameras and photo-editing software has made every picture a potential scam. The skillful user of Adobe Photoshop and other tools can produce realistic images of imaginary scenes. As Dartmouth University computer scientists Alin Popescu and Hany Farid note, the Los Angeles Times unwittingly ran an altered photograph from the war in Iraq on its front page. To help defend against these types of forgeries, Popescu and Farid have published a new image-processing algorithm that detects photographic fakery.
Methods and Results: When forgers modify an image, they often insert elements taken from other photographs or from other sections of the same photograph; these insertions need to be distorted, resized, or rotated to fit in with the rest of the image. Even when no new elements are added, digital manipulations may leave telltale signs of “re-sampling.” For example, to double an image’s size, software inserts a new pixel between every pair of neighboring pixels in the original image. The new pixels are a combination of the pixels surrounding them in the original image – the result of interpolation. Such regularity rarely occurs in natural images and often produces patterns that Popescu and Farid’s software can detect, even when they’re unapparent to the naked eye. In trials employing 50 images selected at random from a database of 200, Popescu and Farid’s method found nearly all cases of enlargement greater than 1 percent and most cases of rotation that required interpolation. Some cases of shrinking could also be detected.
Why it Matters: Current forgery detection techniques, which are vital for screening news items and intelligence, leave much to be desired. Digital watermarking works only when someone has had the foresight to insert hidden information into an image file to prevent tampering. In contrast, Popescu and Farid’s method can be applied automatically to any image file. However, the method is not foolproof: for example, it cannot detect cases of shrinking without interpolation. Also, data compression, used in JPEG files, and noise interfere with the algorithm. Nonetheless, the new software makes it harder for a digital photograph to lie.
Source: Popescu, A. C. and H. Farid. 2005. Exposing digital forgeries by detecting traces of re-sampling. IEEE Transactions on Signal Processing (in press).