Digital Camera Identification From Sensor Pattern Noise

Lukáš, J.; Fridrich, Jessica; Goljan, Miroslav

doi:10.1109/tifs.2006.873602

Cited by 1,140 publications

(859 citation statements)

References 6 publications

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“…Let the original image be I, we apply a de-noise filter, denoted d(·), to get its smoothed version I D . Here, d(·) is a wavelet based adaptive filter, which has been proved to be effective for images contaminated by common noise [9]. We define noise residual N as the difference between I and I D .…”

Section: Estimation Sample Sets Collection and Distance Metrics Definmentioning

confidence: 99%

A Novel Image Noise Level Function Estimation Approach using Camera Response Function Constraint

Yao

2016

MATEC Web of Conferences

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Abstract. In this paper, we propose a noise level evaluation method for real captured photos. Different from conventional noise removal methods that assume noise follow a simply additive Gaussian distribution, noise distribution in our method is supposed to be a more sophisticated intensity-dependent distribution, which has a better fit with actual noise model. Follow the definition of noise level function (NLF) which represents the variation of the standard deviation of noise with respect to image intensity. After exposing the close relationship between NLF and camera response function (CRF), we fit the curve of NLF with the constraints imposed by the shape of CRF.

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Section: Estimation Sample Sets Collection and Distance Metrics Definmentioning

confidence: 99%

A Novel Image Noise Level Function Estimation Approach using Camera Response Function Constraint

Yao

2016

MATEC Web of Conferences

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“…In this section, we describe the method of SCI proposed in [1]. This approach also forms the fundamental framework for our method.…”

Section: Spn-based Source Camera Identificationmentioning

confidence: 99%

“…where the vector u ∈ R N is the array for pixel values of a raw image (grayscale, in this study), and the vector g(u) ∈ R N represents the denoised version of u that can be generated by using a simple Gaussian filter, the wavelet-based filter [6] as used in [1], and so on. The aim of SCI is to decide whether the query image q ∈ R N has been captured by a specific camera c. To solve this problem, the SPN map of the target camera c needs to be learnt in advance for reference.…”

Section: Spn-based Source Camera Identificationmentioning

confidence: 99%

“…One of the hot topics in this area is source camera identification (SCI), in which the unique device used to capture a given query image is determined [1]- [5]. Goljan et al [1], [2] proposed a powerful method for SCI based on sensor pattern noise (SPN) which is inherent and timeinvariant in each pixel. Due to SPNs, some pixels always indicate brighter/darker intensities for the constant amount of incident light.…”

Section: Introductionmentioning

confidence: 99%

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Sensor-Pattern-Noise Map Reconstruction in Source Camera Identification for Size-Reduced Images

Watanabe

Hosaka

Hamamoto

2013

IEICE Trans. Inf. & Syst.

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SUMMARYFor source camera identification, we propose a method to reconstruct the sensor pattern noise map from a size-reduced query image by minimizing an objective function derived from the observation model. Our method can be applied to multiple queries, and can thus be further improved. Experiments demonstrate the superiority of the proposed method over conventional interpolation-based magnification algorithms.

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“…Until recently, the most prominent method to extract the PRNU has been to use a wavelet extraction filter that extracts in the wavelet domain the medium to high frequency subbands in which the PRNU lies [2]. As such, the extracted signature contains a mixture of different types of noises including the desired SPN or PRNU, random noise, fixed pattern noise (FPN) and any high frequency scene details.…”

Section: Introductionmentioning

confidence: 99%

Source identification of camera phones using SVD

Soobhany

Lam

Fletcher

et al. 2013

2013 IEEE International Conference on Image Processing

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We present a novel method to extract the sensor pattern noise (SPN) of digital images and associate them with their respective source camera phones. The method first estimates the photo response non-uniformity (PRNU) of each image by means of its energy level and then converts it to an additive noise to facilitate extraction using Singular Value Decomposition (SVD). The latter is a spectral decomposition technique that separates the PRNU from the signal subspace. The camera reference signatures of the individual cameras are computed from a sample of their respective images and compared with a mixture of image signatures from a set of known camera devices. Our studies show that it is possible to determine the source device of digital images from camera phones using such method of signature extraction, with encouraging results.

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Digital Camera Identification From Sensor Pattern Noise

Cited by 1,140 publications

References 6 publications

A Novel Image Noise Level Function Estimation Approach using Camera Response Function Constraint

A Novel Image Noise Level Function Estimation Approach using Camera Response Function Constraint

Sensor-Pattern-Noise Map Reconstruction in Source Camera Identification for Size-Reduced Images

Source identification of camera phones using SVD

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