Researchers at the University of Albany have developed an efficient and automatic process for identifying composite images, based on the different noise patterns between the two images. In a paper presented at the IEEE's International Conference on Computational Photography, a team led by Siwei Lyu showed they were able to find and locate composited material in images from an online 'Photoshopping' contest site worth1000.com. The team's algorithm exploits the tendency for image noise (regardless of source) to have a characteristic shape (kurtosis). Scanning the image for areas with different noise patterns allows the system to identify non-original content.

The original image from the contest website Worth1000.com The same image, overlaid with the added areas identified by the algorithm.

Because the process is based on analysis of noise, the authors concede it can't identify images with cloning or similar shots from the same camera, and struggles with downsized and heavily-compressed images that have lost their underlying noise characteristics. However, they hope to extend their work by improving its ability to distinguish between local image detail and noise, to improve its accuracy and provide improved noise reduction methods.

The group's research paper can be read here. Non statisticians may wish to look at Figure 4 to see how successful the system was in identifying added-in regions. As the authors point out,'Note that these forgeries are carefully manipulated and processed and have realistic appearance, many can only be exposed based on conceptual knowledge of the physical world (e.g., hippopotamus is unlikely to be found in arctic regions).' Figure 2, which shows the system's success at isolating intentionally introduced noise, hints at the benefits the system could bring to context-sensitive noise-reduction, with further work.