The Pixel Non-Uniformity noise (PNU noise, for short) is a characteristic noise of digital camera sensors that has been originally used a mean to perform Source Camera Identification (SCI), that is, to identify the digital camera that has been used to take an image under scrutiny. Actually, its usage has been extended to other relevant application domains, such as carrying out sensor identification in iris biometrics, to resolve the integrity of a biometric authentication system, in health monitoring systems based on bio-signal processing, and to prevent health fraud scams. As a consequence of this popularity, several counter-forensics techniques have been proposed in the scientific literature to deceive this identification approach and, thus, putting at risk its effectiveness. The goal of this paper is to experiment with some of these counter-forensics techniques and to assess whether they are able or not to deceive the PNU-based identification process. We focus our attention on the particular case where a target image is modified so as to appear as been taken by a different device than the original one (i.e., spoofing). The results of our experiments show that this kind of spoofing is apparently successful in deceiving the PNU-based identification process. However, we also show that the spoofed images retain traces of their originating camera. By leveraging on this information, it is still possible to trace back the device used to take that image. This finding may have a deep impact on the usage of Pixel Non Uniformity (PNU) noise for identification tasks, such as the ones carried out by biometric recognition and monitoring systems, and on the possibility to really cheat them by means of counter-forensics techniques.

PNU Spoofing: a menace for biometrics authentication systems? / Bruno, A.; Cattaneo, G.; Ferraro Petrillo, U.; Capasso, P.. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - 151:(2021), pp. 3-10. [10.1016/j.patrec.2021.07.008]

PNU Spoofing: a menace for biometrics authentication systems?

Ferraro Petrillo U.;
2021

Abstract

The Pixel Non-Uniformity noise (PNU noise, for short) is a characteristic noise of digital camera sensors that has been originally used a mean to perform Source Camera Identification (SCI), that is, to identify the digital camera that has been used to take an image under scrutiny. Actually, its usage has been extended to other relevant application domains, such as carrying out sensor identification in iris biometrics, to resolve the integrity of a biometric authentication system, in health monitoring systems based on bio-signal processing, and to prevent health fraud scams. As a consequence of this popularity, several counter-forensics techniques have been proposed in the scientific literature to deceive this identification approach and, thus, putting at risk its effectiveness. The goal of this paper is to experiment with some of these counter-forensics techniques and to assess whether they are able or not to deceive the PNU-based identification process. We focus our attention on the particular case where a target image is modified so as to appear as been taken by a different device than the original one (i.e., spoofing). The results of our experiments show that this kind of spoofing is apparently successful in deceiving the PNU-based identification process. However, we also show that the spoofed images retain traces of their originating camera. By leveraging on this information, it is still possible to trace back the device used to take that image. This finding may have a deep impact on the usage of Pixel Non Uniformity (PNU) noise for identification tasks, such as the ones carried out by biometric recognition and monitoring systems, and on the possibility to really cheat them by means of counter-forensics techniques.
2021
authentication systems; biometrics; pixel non uniformity; source camera identification
01 Pubblicazione su rivista::01a Articolo in rivista
PNU Spoofing: a menace for biometrics authentication systems? / Bruno, A.; Cattaneo, G.; Ferraro Petrillo, U.; Capasso, P.. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - 151:(2021), pp. 3-10. [10.1016/j.patrec.2021.07.008]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1603737
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