Methods of Weighted Averaging with Application to Biomedical Signals

IEEE Trans.Inform.Forensic Secur.

2017

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 Weighted Averagingmentioning

confidence: 99%

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

IEEE Trans.Inform.Forensic Secur.

2017

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“…In theory, the constant averaging method is optimal only if the noise variance is constant in all observations [26]. The WA method relies on the theory of unknown signal estimation from a number of noisy observations [27]. If the noise variance varies from one observation to another, the WA method can offer the best estimation to the real signal in terms of the mean squared error [26] [27].…”

Section: Proposed Prnu Estimation Approachmentioning

confidence: 99%

PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)

Bouridane

et al. 2022

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one.

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

2014 IEEE International Conference on Image Processing (ICIP)

Bouridane

2014