We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision.
Sensor fingerprinting has received significant attention in recent years, primarily in the context of de-anonymizing photos by correlating them to images with a known source. @cite_6 , images taken by different cameras are processed to derive a reference noise pattern that is specific for each sensor. Based on this pattern, additional images are associated with their most likely source. Noise extraction algorithms are a critical part of this approach, and @cite_29 proposes some further enhancements. The image capture pipeline is investigated in @cite_3 , where different stages of the process are revealed to introduce distinct artifacts. These artifacts can be used to design more robust identification algorithms.
@cite_29 @cite_3 @cite_6
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Sensor pattern noises (SPNs), extracted from digital images to serve as the fingerprints of imaging devices, have been proved as an effective way for digital device identification. However, as we demonstrate in this work, the limitation of the current method of extracting SPNs is that the SPNs extracted from images can be severely contaminated by details from scenes, and as a result, the identification rate is unsatisfactory unless images of a large size are used. In this work, we propose a novel approach for attenuating the influence of details from scenes on SPNs so as to improve the device identification rate of the identifier. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in an SPN is, the less trustworthy the component should be, and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components. The various image-processing stages in a digital camera pipeline leave telltale footprints, which can be exploited as forensic signatures. These footprints consist of pixel defects, of unevenness of the responses in the charge-coupled device sensor, black current noise, and may originate from proprietary interpolation algorithms involved in color filter array. Various imaging device (camera, scanner, etc.) identification methods are based on the analysis of these artifacts. In this paper, we set to explore three sets of forensic features, namely binary similarity measures, image-quality measures, and higher order wavelet statistics in conjunction with SVM classifiers to identify the originating camera. We demonstrate that our camera model identification algorithm achieves more accurate identification, and that it can be made robust to a host of image manipulations. The algorithm has the potential to discriminate camera units within the same model. In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's 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.