Weighted averaging-based sensor pattern noise estimation for source camera identification

Lawgaly, Ashref; Khelifi, Fouad; Bouridane, Ahmed

doi:10.1109/icip.2014.7026084

Cited by 28 publications

(17 citation statements)

References 7 publications

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“…In this section, the proposed system, referred here to as Color LADCT HV 1 +WA, is assessed in comparison with existing state-of-the-art systems, namely Basic PRNU [8], the Maximum Likelihood Estimator MLE 3 [10], Phase PRNU [11], Color Decoupling estimator (CD PRNU) [21], wavelet-based Mihcak's filter followed by Weighted Averaging (Wavelet+WA) [16], and Wiener-median PRNU [19]. The comparative analysis covers three different aspects, i.e., source camera identification, source camera verification, and the purity of PRNU estimation.…”

Section: B Comparison With State-of-the-art Systemsmentioning

confidence: 99%

“…Theoretically, such process could generate a higher quality reduced-size PRNU, which may lead to a more trustworthy PRNU than its original full-size one. In [16] an approach based on a Weighted Averaging (WA) technique to optimize PRNU estimation was applied. The idea is based on the assumption that images are acquired under different conditions making the corresponding residual signals different from each other.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification

Lawgaly

Khelifi

2017

IEEE Trans.Inform.Forensic Secur.

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Abstract-Photo Response Non-Uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for source camera identification and image authentication. The abundant information that the sensor pattern noise carries in terms of the frequency content makes it unique, and hence suitable for identifying the source camera and detecting image forgeries. However, the PRNU extraction process is inevitably faced with the presence of image-dependent information as well as other non-unique noise components. To reduce such undesirable effects, researchers have developed a number of techniques in different stages of the process, i.e., the filtering stage, the estimation stage, and the postestimation stage. In this paper, we present a new PRNU-based source camera identification and verification system and propose enhancements in different stages. First, an improved version of the Locally Adaptive Discrete Cosine Transform (LADCT) filter is proposed in the filtering stage. In the estimation stage, a new Weighted Averaging (WA) technique is presented. The postestimation stage consists of concatenating the PRNUs estimated from color planes in order to exploit the presence of physical PRNU components in different channels. Experimental results on two image datasets acquired by various camera devices have shown a significant gain obtained with the proposed enhancements in each stage as well as the superiority of the overall system over related state-of-the-art systems.

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Section: B Comparison With State-of-the-art Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification

Lawgaly

Khelifi

2017

IEEE Trans.Inform.Forensic Secur.

Self Cite

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show abstract

“…This is due to several reasons among which is the variation of the camera settings such as integration time, shutter speed and focal length at the times of taking the pictures. Relying on this fact, it was proposed in [35] to capitalise on a weighted averaging operation to reduce the estimation error:…”

Section: B Weighted Averaging (Wa)mentioning

confidence: 99%

“…In [35], the noise variances are estimated using the difference signal estimation approach proposed in [36]. Based on the assumption that the PRNU is deterministic and invariant for an individual pixel of a camera from image to another, the random noise component can be obtained by subtracting the noise residual by the .…”

Section: B Weighted Averaging (Wa)mentioning

confidence: 99%