On the Reliability of Cell Phone Camera Fingerprint Recognition

Steinebach, Martin; Ouariachi, Mohamed El; Liu, Huajian; Katzenbeisser, Stefan

doi:10.1007/978-3-642-11534-9_7

Cited by 2 publications

References 4 publications

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“…So the overall quality loss is smaller than it appears. This attack has been replicated several times, for example in [30], and in [46,45] for cell-phone cameras. We are not aware of substantial refinements of this specific attack, for example adaptations to the peak-to-correlation energy detector.…”

Section: Substitution Of Sensor Noise Patternmentioning

confidence: 99%

Counter-Forensics: Attacking Image Forensics

Böhme

Kirchner

2012

Digital Image Forensics

103

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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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Section: Substitution Of Sensor Noise Patternmentioning

confidence: 99%

Counter-Forensics: Attacking Image Forensics

Böhme

Kirchner

2012

Digital Image Forensics

103

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“…One is in term of mobile model that denotes products from different manufacturers. The other means the alternate cell phones of the same model [4][5][6][7][8]. In this study, we focus on the cell phone model identification.…”

Section: Introductionmentioning

confidence: 99%

Classifier Combination Based Source Identification for Cell Phone Images

et al. 2015

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Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone models of the training sets in multi-class classifier, a one-class classifier is orderly used in the framework. Feature vectors including color filter array (CFA) interpolation coefficients estimation and multi-feature fusion is employed to verify the effectiveness of the classifier combination strategy. Experimental results demonstrate that for different feature sets, our method presents high accuracy of source identification both for the cell phone in the training sets and the outliers.

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On the Reliability of Cell Phone Camera Fingerprint Recognition

Cited by 2 publications

References 4 publications

Counter-Forensics: Attacking Image Forensics

Counter-Forensics: Attacking Image Forensics

Classifier Combination Based Source Identification for Cell Phone Images

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