Unsupervised classification of digital images using enhanced sensor pattern noise

Li, Chang-Tsun

doi:10.1109/iscas.2010.5537850

Cited by 50 publications

(81 citation statements)

References 5 publications

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“…To verify the performances of the presented blind clustering procedure the system has been tested on a dataset populated by 1200 photos at different resolutions (from 3MP to 12MP) taken by six cameras in different time period (200 photos [1]. • image blocks of different sizes, from 128×128 to 1536× 2048 pixels;…”

Section: Methodsmentioning

confidence: 99%

“…Since Photo Response Non-Uniformity (PRNU) is part of the high-frequency components of the image's signal, it can be used the model in Equation (1), in order to extract the noise n from an image I:…”

Section: Prnu Enhancermentioning

confidence: 99%

“…Starting from the method proposed in [1], it has been attempted to improve performance in terms of computation speed and accuracy estimation. To do this a different clustering algorithm based on hierarchical clustering has been introduced [17].…”

Section: Fast Unsupervised Clusteringmentioning

confidence: 99%

“…Furthermore, not to get misleading evaluation of performances, overlapping between the two data-set has been avoided. Then the TPR (True Positive Rate) and the processing time for the proposed algorithm (with and without enhancer) are compared with the method in [1]. The TPR related to the training phase (or clustering phase) obtained by varying the image block dimension is reported in Table 1.…”

Section: Time Vs Sizementioning

confidence: 99%

“…Starting from a previous work [1] we have implemented a new enhancer function to improve PRNU quality and then a new fast clustering procedure is presented. Such clustering technique permits both to reduce computational complexity and to improve the true positive rate (TPR) in image grouping.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Prnu Enhancermentioning

confidence: 99%

Section: Fast Unsupervised Clusteringmentioning

confidence: 99%

Section: Time Vs Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Anti‐noise image source identification

Liu

Huang

Zhang

et al. 2018

Concurrency and Computation

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Summary Image source identification plays an important role in image forensics. Traditional image source identification techniques rely on the assumption that the training datasets are well labeled. However, in the real world, this assumption may not be the truth, and some noisy samples may exist in the training datasets. In this paper, firstly, we theoretically investigate the influence of noisy samples to the identification performance. Then, a new image source identification approach, namely, Anti‐noise Image Source Identification (AISI), is proposed to deal with those noisy samples. AISI has three steps, ie, noise level evaluation, noise level based sampling, and multi‐classification. Noise level evaluation aims to assign each sample with a noise level that indicates the probability of being noisy. The basic idea of noise level based sampling is to sample images according to their noise levels. We provide a theoretically justification to demonstrate the effectiveness of AISI. Experiments conducted on a real‐world image collection confirm that the proposed AISI can alleviate the influence of noisy samples and can improve the identification accuracy while noise exists.

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