Adaptive photo-response non-uniformity noise removal against image source attribution

Karaküçük, Ahmet; Dirik, Ahmet Emir

doi:10.1016/j.diin.2015.01.017

Cited by 45 publications

(30 citation statements)

References 16 publications

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“…If α is set too high, Alice's chances of running a successful triangle test may increase substantially [4], [7]. Before and after superimposing the fingerprint, Eve may apply further processing steps to make her forgery more compelling, e. g., removing the genuine camera fingerprint [10], synthesizing demosaicing artifacts [11], and removing or adding traces of JPEG compression [12].…”

Section: B Fingerprint-copy Attackmentioning

confidence: 99%

Fragile sensor fingerprint camera identification

Quiring

Kirchner

2015

2015 IEEE International Workshop on Information Forensics and Security (WIFS)

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We study digital camera identification based on sensor noise in an adversarial environment with asymmetries. We focus on fingerprint-copy attacks, where the attacker has access to JPEG images only, while the defender may leverage uncompressed images. This leads to the notion of fragile sensor fingerprints that are only available to the defender but do not survive lossy compression. Experiments with seven different cameras suggest a highly reliable detection of the attack as long as no high-quality images are shared with the public.

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Section: B Fingerprint-copy Attackmentioning

confidence: 99%

Fragile sensor fingerprint camera identification

Quiring

Kirchner

2015

2015 IEEE International Workshop on Information Forensics and Security (WIFS)

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“…• Destroying the Image Identity: This class of counter forensic techniques tries to conceal the identity of an image and therefore prevents an identification of the image source or camera, respectively. Some examples are: removing the PRNU [82], [83], [84], [85], seam carving [86], [87], adaptive PRNU denoising [88]. Applying these techniques to morphed face images poses a lower threat to a PRNU-based morph detection system, since the aim is not to detect the image source, but to analyse the general properties of the PRNU signal.…”

Section: B Potential Attacks and Prnu Robustnessmentioning

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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“…However, the attack's success generally depends on a good choice of α: too low values mean that the bogus image J may not be assigned to Alice's camera; a too strong embedding will make the image appear suspicious [13,20]. In practical scenarios, Mallory may have to apply further processing to make her forgery more compelling, e. g., removing the genuine camera fingerprint [7,16], synthesizing demosaicing artifacts [17], and removing or adding traces of JPEG compression [25].…”

Section: Fingerprint-copy Attackmentioning

confidence: 99%

On the Security and Applicability of Fragile Camera Fingerprints

Quiring

Kirchner

Rieck

2019

Lecture Notes in Computer Science

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Camera sensor noise is one of the most reliable device characteristics in digital image forensics, enabling the unique linkage of images to digital cameras. This so-called camera fingerprint gives rise to different applications, such as image forensics and authentication. However, if images are publicly available, an adversary can estimate the fingerprint from her victim and plant it into spurious images. The concept of fragile camera fingerprints addresses this attack by exploiting asymmetries in data access: While the camera owner will always have access to a full fingerprint from uncompressed images, the adversary has typically access to compressed images and thus only to a truncated fingerprint. The security of this defense, however, has not been systematically explored yet. This paper provides the first comprehensive analysis of fragile camera fingerprints under attack. A series of theoretical and practical tests demonstrate that fragile camera fingerprints allow a reliable device identification for common compression levels in an adversarial environment.

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Adaptive photo-response non-uniformity noise removal against image source attribution

Cited by 45 publications

References 16 publications

Fragile sensor fingerprint camera identification

Fragile sensor fingerprint camera identification

Detection of Face Morphing Attacks Based on PRNU Analysis

On the Security and Applicability of Fragile Camera Fingerprints

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