In this paper, we study the problem of identifying digital camera sensor from a printed picture. The sensor is identified
by proving the presence of its Photo-Response Non-Uniformity (PRNU) in the scanned picture using camera ID
methods robust to cropping and scaling. Two kinds of prints are studied. The first are postcard size (4" by 6") pictures
obtained from common commercial printing labs. These prints are always cropped to some degree. In the proposed
identification, a brute force search for the scaling ratio is deployed while the position of cropping is determined from
the cross-correlation surface. Detection success mostly depends on the picture content and the quality of the PRNU
estimate. Prints obtained using desktop printers form the second kind of pictures investigated in this paper. Their
identification is complicated by complicated geometric distortion due to imperfections in paper feed. Removing this
distortion is part of the identification procedure. From experiments, we determine the range of conditions under which
reliable sensor identification is possible. The most influential factors in identifying the sensor from a printed picture
are the accuracy of angular alignment when scanning, printing quality, paper quality, and size of the printed picture.
Photo-response non-uniformity (PRNU) of digital sensors was recently proposed  as a unique identification fingerprint
for digital cameras. The PRNU extracted from a specific image can be used to link it to the digital camera that took the
image. Because digital camcorders use the same imaging sensors, in this paper, we extend this technique for
identification of digital camcorders from video clips. We also investigate the problem of determining whether two video
clips came from the same camcorder and the problem of whether two differently transcoded versions of one movie came
from the same camcorder. The identification technique is a joint estimation and detection procedure consisting of two
steps: (1) estimation of PRNUs from video clips using the Maximum Likelihood Estimator and (2) detecting the presence
of PRNU using normalized cross-correlation. We anticipate this technology to be an essential tool for fighting piracy of
motion pictures. Experimental results demonstrate the reliability and generality of our approach.
We present a new approach to detection of forgeries in digital images under the assumption that either the camera that took the image is available or other images taken by that camera are available. Our method is based on detecting the presence of the camera pattern noise, which is a unique stochastic characteristic of imaging sensors, in individual regions in the image. The forged region is determined as the one that lacks the pattern noise. The presence of the noise is established using correlation as in detection of spread spectrum watermarks. 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.
In this paper, we demonstrate that it is possible to use the sensor’s pattern noise for digital camera identification from images. The pattern noise is extracted from the images using a wavelet-based denoising filter. For each camera under investigation, we first determine its reference noise, which serves as a unique identification fingerprint. This could be done using the process of flat-fielding, if we have the camera in possession, or by averaging the noise obtained from multiple images, which is the option taken in this paper. To identify the camera from a given image, we consider the reference pattern noise as a high-frequency spread spectrum watermark, whose presence in the image is established using a correlation detector. Using this approach, we were able to identify the correct camera out of 9 cameras without a single misclassification for several hundred images. Furthermore, it is possible to perform reliable identification even from images that underwent subsequent JPEG compression and/or resizing. These claims are supported by experiments on 9 different cameras including two cameras of exactly same model (Olympus C765).