Natural Image Statistics in Digital Image Forensics

Over the past decade, with the increasing popularity of Internet and digital technology, images in digital format have become ubiquitous. At the same time, with the break-neck speeds of the development of technology that allows for digital images to be manipulated and distorted, detecting tampering or validating authenticity of digital images are of great importance for forensic practitioners. This book provides the first general framework, based on universal statistical properties of natural images, of detecting tampering and authenticating digital images that has been successfully applied to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) detection of steganography (hidden messages) in digital images; (3) differentiating lively captured and rebroadcast images in biometric-based authentication systems, and also to digital authentication and identification in art forensics. This book should help bridging the research work in image modeling and forensics, and should be especially useful to researchers and practitioners in image modeling, digital image forensics or related fields.

The author is an assistant professor in computer science at State University of New York, Albany. He obtained his Ph.D. degree in computer science from Dartmouth College in 2005, From 2005 to 2007 he was a post-doctoral research associate at New York University. His research interests include image processing, image forensics, and computer vision.