Jan Lukás

Digital camera identification from sensor pattern noise

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensors pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine 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.

Determining Image Origin and Integrity Using Sensor Noise

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

Imaging sensor noise as digital X-ray for revealing forgeries

In this paper, we describe a new forensic tool for revealing digitally altered images by detecting the presence of photo-response nonuniformity noise (PRNU) in small regions. This method assumes that either the camera that took the image is available to the analyst or at least some other nontampered images taken by the camera are available. Forgery detection using the PRNU involves two steps - estimation of the PRNU from non-tampered images and its detection in individual image regions. From a simplified model of the sensor output, we design optimal PRNU estimators and detectors. Binary hypothesis testing is used to determine which regions are forged. The method is tested on forged images coming from a variety of digital cameras and with different JPEG quality factors. The approximate probability of falsely identifying a forged region in a non-forged image is estimated by running the algorithm on a large number of non-forged images.

Detecting digital image forgeries using sensor pattern noise

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.

Determining digital image origin using sensor imperfections

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 pattern, 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 thousand 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 the same model.

Source digital camcorder identification using sensor photo response non-uniformity

Photo-response non-uniformity (PRNU) of digital sensors was recently proposed [1] 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.