Determining digital image origin using sensor imperfections

Abstract

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 camera identification based on CFA interpolation

“…Another promising approach in this area is made by Lukas et al [5]. In their work, sensor's pattern noise is characterized via wavelet-based image denoising.…”

A Comprehensive Study of Passive Digital Image Forensics Techniques based on Intrinsic Fingerprints

“…Although every camera has a unique noise pattern but the pattern noises of different cameras have some common statistical properties that are not present in computer generated images and can be used to classify the images between natural and synthetic images. Similarly the images generated by different computer graphics software will exhibit common properties that will not be present in images captured by cameras [16].…”

“…This unique sensor noise pattern can be uniquely mapped to a source digital camera using the reference error pattern which is calculated by averaging noise pattern calculated using denoising process over a number of images captured by that particular camera [16].…”

Forensic classification of imaging sensor types

“…One may want to find the particular device (digital camera or scanner) which generated the image or one might be interested in knowing only the make and model of the device. As summarized in [2], a number of interesting and robust methods have been proposed for source camera identification [3][4][5][6].…”

“…In [7], it is shown that defective pixels can be used for reliable camera identification even from lossy compressed images. In [6], an approach for camera identification using the imaging sensor's pattern noise was presented. The identification is based on pixel nonuniformity noise which is a unique stochastic characteristic for both CCD (Charged Coupled Device) and CMOS (Complementary Metal Oxide Semiconductor) sensors.…”