Enhancing Source Camera Identification Performance With a Camera Reference Phase Sensor Pattern Noise

Kang, Xiangui; Li, Yinxiang; Qu, Zhenhua; Huang, Jiwu

doi:10.1109/tifs.2011.2168214

Cited by 197 publications

(112 citation statements)

References 12 publications

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“…Li [10] demonstrated that the SPN extracted from a single image can be contaminated by image scene details and proposed some models to attenuate the strong signal component of noise residue. However, attenuating strong components from scene details may also attenuate the useful SPN components [11]. Kang et al [11] proposed a detection statistic correlation over circular correlation norm (CCN) to lower the false-positive rate and a whitecamera reference SPN to enhance the ROC performance [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, attenuating strong components from scene details may also attenuate the useful SPN components [11]. Kang et al [11] proposed a detection statistic correlation over circular correlation norm (CCN) to lower the false-positive rate and a whitecamera reference SPN to enhance the ROC performance [12]. The noise residues extracted from the original images are whitened first and then averaged to generate the white-camera phase reference SPN.…”

Section: Introductionmentioning

confidence: 99%

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A context-adaptive SPN predictor for trustworthy source camera identification

et al. 2014

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Sensor pattern noise (SPN) has been recognized as a reliable device fingerprint for camera source identification (CSI) and image origin verification. However, the SPN extracted from a single image can be contaminated largely by image content details from scene because, for example, an image edge can be much stronger than SPN and hard to be separated. So, the identification performance is heavily dependent upon the purity of the estimated SPN. In this paper, we propose an effective SPN predictor based on eight-neighbor context-adaptive interpolation algorithm to suppress the effect of image scene and propose a source camera identification method with it to enhance the receiver operating characteristic (ROC) performance of CSI. Experimental results on different image databases and on different sizes of images show that our proposed method has the best ROC performance among all of the existing CSI schemes, as well as the best performance in resisting mild JPEG compression, especially when the false-positive rate is held low. Because trustworthy CSI must often be performed at low false-positive rates, these results demonstrate that our proposed technique is better suited for use in real-world scenarios than existing techniques. However, our proposed method needs many such as not less than 100 original images to create camera fingerprint; the advantage of the proposed method decreases when the camera fingerprint is created with less original images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A context-adaptive SPN predictor for trustworthy source camera identification

et al. 2014

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“…One is the accuracy of the fingerprints extracted from images. Various forms of device fingerprints such as sensor pattern noise (SPN) [1][2][3][4][5][6][7][8][9][10][11][12], camera response function [13], re-sampling artifacts [14], color filter array (CFA) interpolation artifacts [15,16], JPEG compression [17], and lens aberration [12,18] have been proposed in recent years. Other device and image attributes such as binary similarity measures, image quality measures, and higher order wavelet statistics have also been adopted for identifying source imaging devices [19][20][21][22].…”

Section: The Challenges Of Source-oriented Image Clustering and Relatmentioning

confidence: 99%

“…While many methods [13][14][15][16] make specific assumptions in their applications, SPN-based methods [1][2][3][4][5][6][7][8][9][10][11][12] do not require such assumptions to be satisfied and thus have drawn much more attention. Another merit of SPN is that it is unique to each device, which means it is capable of differentiating individual devices of the same model [1,3,5,11]. These merits make SPN a good candidate for various digital forensic applications.…”

Section: The Challenges Of Source-oriented Image Clustering and Relatmentioning

confidence: 99%

A fast source-oriented image clustering method for digital forensics

Lin

2017

J Image Video Proc.

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We present in this paper an algorithm that is capable of clustering images taken by an unknown number of unknown digital cameras into groups, such that each contains only images taken by the same source camera. It first extracts a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. The image clustering is performed based on the pairwise correlations between camera fingerprints extracted from images. During this process, each SPN is treated as a random variable and a Markov random field (MRF) approach is employed to iteratively assign a class label to each SPN (i.e., random variable). The clustering process requires no a priori knowledge about the dataset from the user. A concise yet effective cost function is formulated to allow different "neighbors" different voting power in determining the class label of the image in question depending on their similarities. Comparative experiments were carried out on the Dresden image database to demonstrate the advantages of the proposed clustering algorithm.

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“…Hu et al [3] argued that the large or principal components of noise residue are more robust against random noise, so instead of using the full-length SPN, only a small portion of the largest components are involved in the calculation of correlation. Kang et al [4] introduced a camera reference phase SPN to remove the periodic noise and other non-white noise contamination in the camera fingerprint. They proposed to use the correlation over circular cross-correlation norm (CCN) to further suppress the impact of periodic noise contamination.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sensor Pattern Noise via Filtering Distortion Removal

Lin

2016

IEEE Signal Process. Lett.

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Enhancing Source Camera Identification Performance With a Camera Reference Phase Sensor Pattern Noise

Cited by 197 publications

References 12 publications

A context-adaptive SPN predictor for trustworthy source camera identification

A context-adaptive SPN predictor for trustworthy source camera identification

A fast source-oriented image clustering method for digital forensics

Enhancing Sensor Pattern Noise via Filtering Distortion Removal

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