Determining Image Origin and Integrity Using Sensor Noise

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

doi:10.1109/tifs.2007.916285

Cited by 796 publications

(726 citation statements)

References 32 publications

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“…It can be used to identify source devices as well as to determine whether an image has been tampered with [13]. The PRNU is due to imperfections arising from the manufacturing process of the sensor and due to slight variations in conversion of light to electrical energy by individual pixel sensors [14].…”

Section: Signature Extraction Of Spn/prnumentioning

confidence: 99%

“…For mobile phones, in addition, it can also be contaminated by the blockiness (row/column noise) created by the JPEG compression and other processing operations performed in the camera pipeline. Consequently, further processing is often applied to facilitate the estimation of the SPN, including the attenuation of nonunique artefacts (NUA) such as the FPN, blockiness and colour interpolation [13]. The accuracy of SPN can also be improved by attenuating the interference of scene details with the enhancer described in [3], where the enhanced SPN was shown to increase the identification rate and allows the use of smaller image crop size.…”

Section: Signature Extraction Of Spn/prnumentioning

confidence: 99%

See 1 more Smart Citation

Mobile Camera Source Identification with SVD

Soobhany

Lam

Fletcher

et al. 2014

Lecture Notes in Electrical Engineering

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Abstract-A novel method for extracting the characterising sensor pattern noise (SPN) from digital images is presented. Based on the spectral decomposition technique of Singular Value Decomposition, the method estimates the SPN of each image in terms of its energy level by first transforming the image/signals into a linear additive noise model that separates the photo response non-uniformity (PRNU) of the associated camera 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. The statistical properties of the method were studied using the Student's t-test constructed under the null hypothesis formalism. 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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Section: Signature Extraction Of Spn/prnumentioning

confidence: 99%

Section: Signature Extraction Of Spn/prnumentioning

confidence: 99%

Mobile Camera Source Identification with SVD

Soobhany

Lam

Fletcher

et al. 2014

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…For mobile phones, in addition, it can also be contaminated by the blockiness (row/column noise) created by the JPEG compression and other processing operations performed in the camera pipeline. Consequently, further processing is often applied to facilitate the estimation of the SPN, including the attenuation of non-unique artefacts (NUA) such as the FPN, blockiness and colour interpolation [13]. The accuracy of SPN can also be improved by attenuating the interference of scene details with the enhancer described in [3], where the enhanced SPN was shown to increase the identification rate and allows the use of smaller image crop size.…”

Section: Signature Extraction Of Spn/prnumentioning

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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“…Scientific instruments which allow to give answers to basic questions regarding image origin and image authenticity are needed [2]. Both these issues are anyway connected and sometimes are investigated together.…”

Section: Introductionmentioning

confidence: 99%

“…images scanned by a particular scanner, photos taken by a camera and so on). Usually fingerprints are computed by means of the extraction of PRNU noise (Photo Response Non-Uniformity) [2], [11], [12], [13] through a digital filtering operation; PRNU presence is induced by intrinsic disconformities in the manufacturing process of silicon CCD/CMOSs. After that the PRNU of the to-be-checked content is compared with the fingerprints and then it is classified.…”

Section: Introductionmentioning

confidence: 99%

Fast image clustering of unknown source images

Caldelli

Amerini

Picchioni

et al. 2010

2010 IEEE International Workshop on Information Forensics and Security

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Abstract-Succeeding in determining information about the origin of a digital image is a basic issue of multimedia forensics.In particular it could be interesting to individuate which is the specific camera (brand and/or model) that has taken that photo; to do that, additional knowledge are needed about the camera such as its fingerprint, usually computed by resorting at the extraction of the PRNU (Photo-Response-Uniformity-Noise) by using a group of images coming from that camera. It is easy to understand that in many application scenarios information at disposal are very limited; this is the case when, given a set of N images, we want to establish if they belong to M different cameras where M is less or, at most, equal to N, without having any knowledge about the source cameras. In this paper a new technique which aims at blindly clustering a given set of N digital images is presented. Such a technique is based on a pre-existing one [1] and improves it both in terms of error probability and of computational efficiency. The system is able, in an unsupervised and fast manner, to group photos without any initial information about their membership. Sensor pattern noise is extracted by each image as reference and the successive classification is performed by means of a hierarchical clustering procedure. Experimental results have been carried out to verify theoretical expectations and to witness the improvements with respect to the other technique. Tests have also been done in different operative circumstances (e.g. asymmetric distribution of the images within each cluster), obtaining satisfactory results.

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Determining Image Origin and Integrity Using Sensor Noise

Cited by 796 publications

References 32 publications

Mobile Camera Source Identification with SVD

Mobile Camera Source Identification with SVD

Source identification of camera phones using SVD

Fast image clustering of unknown source images

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