| In this dissertation, we propose new digital forensics methods based on detection of sensor's pattern noise. We use this methodology for identifying digital image origin, its authenticity, and whether it was tampered with or not.; We first describe the principles of digital camera operations, the imaging sensor types, and different types of noise occurring in digital imaging sensors. Then, the pattern noise, its components, and means for its extraction are described. All our methods rely on imaging device's reference pattern (an approximation to the device's pattern noise), which serves as a unique identification fingerprint. This reference pattern is acquired by averaging noise obtained from multiple images from the device using a denoising filter. The pattern noise can be considered as a spread spectrum watermark. We therefore detect its presence by a correlation with the reference pattern.; To identify the imaging device from a given image, we simply look for the presence of the device's pattern noise in the image. Experiments on approximately 320 images taken with 9 consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.; When exposing digital forgeries, the forged region is determined as the one that lacks the pattern noise. We proposed two approaches. In the first one, the user selects an area for integrity verification. The second method attempts to automatically determine the forged area without assuming any a priori knowledge. The methods are tested both on examples of real forgeries and on non-forged images. We also investigate how further image processing applied to the forged image, such as lossy compression or filtering, influences our ability to verify image integrity. |
