Performance comparison of denoising filters for source camera identification

Cortiana, A.; Conotter, Valentina; Boato, Giulia; Natale, Francesco G. B. De

doi:10.1117/12.872489

Cited by 38 publications

(30 citation statements)

References 13 publications

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“…Furthermore, all model parameters are chosen the same as those in Li's work, and we use model 3 or model 5 to denote the image noise residue attenuated by model 3 or model 5 in our results. As a result, we compare our PCAI8 method with the MLE method from [7], BM3D method [5], PCAI4 method [13], phase method [11], and Li's method [10] (i.e., model 3 and model 5).…”

Section: Resultsmentioning

confidence: 99%

“…A camera reference SPN is built by averaging residual noise from multiple images taken by the same camera. In [5], an innovative and recently introduced denoising filter, namely, a sparse 3D transform-domain collaborative filtering (BM3D) [6], is used to extract the SPN. This filter is based on an enhanced sparse representation in a transform domain.…”

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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A context-adaptive SPN predictor for trustworthy source camera identification

et al. 2014

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“…Since the filtering stage contributes significantly to the accuracy of PRNU estimation, the influence of denoising filter has been discussed in [29] for forgery detection and [30] for source camera identification. The authors show that the Blockmatching and 3D filtering (BM3D) algorithm [31] outperforms the wavelet-based Mihcak's filter [32] which was initially adopted in [8].…”

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.

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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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“…Whilst the authors' primary aim is to adopt a simple, fast de-noising operation in PRNU extraction, they also seek a more accurate estimate of the PRNU compared to other filters. 3) Block-matching and 3D (BM3D) algorithm: The filter, that was introduced in [31], has been explored in PRNU estimation in [32] and [33]. The filter combines slidingwindow transform processing with block-matching, where a pixel of the true image is estimated from regions which are found similar to the region centered at the estimated pixel.…”

Section: ) Total Variation (Tv) Filtermentioning

confidence: 99%