On the Repudiability of Device Identification and Image Integrity Verification Using Sensor Pattern Noise

Li, Chang-Tsun; Chang, Chih-Hao; Li, Yue

doi:10.1007/978-3-642-11530-1_3

Cited by 19 publications

(20 citation statements)

References 11 publications

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“…In this paper, we propose an image source anonymization method against PRNU based camera identification and provide substantial performance benchmarks for existing adaptive source anonymization techniques (Li et al, 2010;Dirik and Karaküçük, 2014). The ultimate goal of the source anonymization against PRNU based source camera identification is to decrease the PCE metric lower than the decision threshold without introducing any visual artifacts.…”

Section: Resultsmentioning

confidence: 99%

“…Adaptive fingerprint removal was first suggested in (Luk a s et al, 2006), then evaluated by Li et al (2010). This approach assumes a known PRNU fingerprint estimate b F of a particular imaging sensor is latent in any given image intensity Y taken by the same sensor.…”

Section: Adaptive Fingerprint Removalmentioning

confidence: 99%

“…Seam-carving (Avidan and Shamir, 2007) impedes SCI by destroying pixel-to-pixel synchronization but also changes the aspect ratio of the image, which may not be desirable for some cases (Bayram et al, 2013). Adaptive fingerprint removal is also indicated as an effective method to remove the PRNU of images in (Luk a s et al, 2006), and this claim was investigated in (Li et al, 2010). However, the robustness of this method has not been evaluated in a setting where an adversary or an analyst is able to obtain high quality and different fingerprints, i.e.…”

Section: Introductionmentioning

confidence: 96%

“…Therefore, PRNU noise can be used to pinpoint the camera device which could be undesirable for some users, such as photographers and activists who want to protect their privacy and preserve their anonymity while sharing or spreading images (B€ ohme and Kirchner, 2013). Such practices of PRNU noise can be nullified by counterforensics techniques, such as flat-fielding (Gloe et al, 2007;B€ ohme and Kirchner, 2013), seam-carving (Bayram et al, 2013;, adaptive fingerprint removal (Luk a s et al, 2006;Li et al, 2010), or adaptive PRNU denoising (Dirik and Karaküçük, 2014). Among these methods, flat-fielding requires specifically captured images, called "flat-fields" to capture the PRNU of imaging sensors and it is sensitive to JPEG compression (Dirik and Karaküçük, 2014).…”

Section: Introductionmentioning

confidence: 97%

“…The robustness of the proposed method to realistic high quality fingerprint attacks was also evaluated experimentally. The PRNU removal performance of the proposed technique was investigated with experimental analysis and compared with the existing counter forensic techniques of Li et al (2010) and Dirik and Karaküçük (2014).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Karaküçük

Dirik

2015

Digital Investigation

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Section: Resultsmentioning

confidence: 99%

Section: Adaptive Fingerprint Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Karaküçük

Dirik

2015

Digital Investigation

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Counter-Forensics: Attacking Image Forensics

Böhme

Kirchner

2012

Digital Image Forensics

115

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This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter.

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