Image data are analyzed for clues to source cameras, authenticity, and integrity.

A program of research in the forensic analysis of digital images has yielded several proposed techniques for automated image-data processing to answer questions concerning the source, authenticity, and integrity of a given image or set of images. The need for such techniques arises because the ease with which digital images can be created and altered without leaving obvious traces can give rise to doubts about their credibility, especially when they are used as legal evidence. Like other proposed techniques of image forensics, the techniques reported here are subject to limitations. Because none of the techniques by itself offers a definitive solution to the digital-image-verification problem, the research continues in an effort to propose new techniques and combine them with existing techniques to obtain more reliable decisions.

The techniques now proposed can be broadly categorized as addressing three primary concerns: (1) identification of source cameras, (2) detection of synthetic images, and (3) detection of images that have been forged or altered. The techniques and the research pertinent thereto are summarized as follows:

•Identifying Source-Camera Models via Image Features

One approach to identification of the camera model that is the source of a given image or set of images is inspired by the success of universal steganalysis techniques. This approach involves analysis of a total of 34 image features to identify certain combinations of features characteristic of specific camera models. The features include color features, image-quality metrics, and wavelet-coefficient statistics. These features are used to construct multi-class classification algorithms. Experiments on several different sets of digital cameras of various models (including cameras in cellular telephones) resulted in identification accuracies ranging from 83 to 97 percent.