A novel image filtering approach for sensor fingerprint estimation in source camera identification

Al-Ani, Mustafa; Khelifi, Fouad; Lawgaly, Ashref; Bouridane, Ahmed

doi:10.1109/avss.2015.7301808

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…This method [23] capitalises on this observation in spatial domain filtering. It counts on engaging as little as one adjacent pixel at estimating the PRNU at a pixel location in order to suppress the correlation between neighbouring pixels in the estimated (supposedly white) signal.…”

Section: -Pixel Approachmentioning

confidence: 96%

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

Al-Ani

Khelifi

2017

IEEE Trans.Inform.Forensic Secur.

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Section: -Pixel Approachmentioning

confidence: 96%

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

Al-Ani

Khelifi

2017

IEEE Trans.Inform.Forensic Secur.

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“…Since the PRNU extraction is relying on a denoising of the image, the resulting pattern might be contaminated with different signals, such as other high frequency image components, e.g., edges, or different types of nonunique artefacts (NUAs) [67]. Many alternative PRNU extraction schemes [68], [69], [70], [71], [72], [73], [74] and PRNU enhancements [75], [76], [77], [78], [79] have been proposed in literature to attenuate different types of PRNU contaminations and improve the quality of the extracted PRNU in source camera identification scenarios. However, to the best of our knowledge, their impact on the general properties of the PRNU signal has not yet been extensively investigated.…”

Section: A Prnu Extraction and Analysismentioning

confidence: 99%

Detection of Face Morphing Attacks Based on PRNU Analysis

Scherhag

Debiasi

Rathgeb

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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Recent research found that attacks based on morphed face images, i.e., morphing attacks, pose a severe security risk to face recognition systems. A reliable morphing attack detection from a single face image remains a research challenge since cameras and morphing techniques used by an attacker are unknown at the time of classification. These issues are commonly overseen while many researchers report encouraging detection performance for training and testing morphing attack detection schemes on images obtained from a single face database employing a single morphing algorithm. In this work, a morphing attack detection system based on the analysis of Photo Response Non-Uniformity (PRNU) is presented. More specifically, spatial and spectral features extracted from PRNU patterns across image cells are analyzed. Differences of these features for bona fide and morphed images are estimated during a threshold-selection stage using the Dresden image database which is specifically built for PRNU analysis in digital image forensics. Cross-database evaluations are then conducted employing an ICAO compliant subset of the FRGCv2 database and a Print-Scan database which is a printed and scanned version of said FRGCv2 subset. Bona fide and morphed face images are automatically generated employing four different morphing algorithms. The proposed PRNU-based morphing attack detector is shown to robustly distinguish bona fide and morphed face images achieving an average D-EER of 11.2% in the best configuration. In scenarios where image sources and morphing techniques are unknown, it is shown to significantly outperform other previously established morphing attack detectors. Finally, the limitations and potential of the approach are demonstrated on a dataset of printed and scanned bona fide and morphed face images.

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“…Various denoising filters have also been evaluated in the literature. These include a context-adaptive interpolator (CAI) [5], 2-pixel approach [22], adaptive spatial (AS) filtering [23], content adaptive guided image (CAGI) filtering [24], and block-matching and 3D (BM3D) algorithm [12], [25]. A recent method proposed usage of a convolutional neural network (CNN) to improve the estimation of noise residual extracted by traditional means [26].…”

Section: Background and Related Workmentioning

confidence: 99%

Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

Joshi¹,

Korus²,

Khan³

et al. 2020

Preprint

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We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e.g., different camera ISP or digital darkroom software). We show that camera fingerprints exhibit non-negligible variations in this setup, which may lead to unexpected degradation of detection statistics in real-world use-cases. We tested 13 different pipelines, including standard digital darkroom software and recent neural-networks. We observed that correlation between fingerprints from mismatched pipelines drops on average to 0.38 and the PCE detection statistic drops by over 40%. The degradation in error rates is the strongest for small patches commonly used in photo manipulation detection, and when neural networks are used for photo development. At a fixed 0.5% FPR setting, the TPR drops by 17 ppt (percentage points) for 128 px and 256 px patches.

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A novel image filtering approach for sensor fingerprint estimation in source camera identification

Cited by 17 publications

References 10 publications

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

Detection of Face Morphing Attacks Based on PRNU Analysis

Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

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