Camera identification is a well known problem in image forensics, addressing the issue to identify the camera a digital image has been shot by. In this paper, we pose our attention to the task of clustering images, belonging to a heterogenous set, in groups coming from the same camera and of doing this in a blind manner; this means that side information neither about the sources nor, above all, about the number of expected clusters is requested. A novel methodology based on Normalized Cuts (NC) criterion is presented and evaluated in comparison with other state-of-the-art techniques, such as Multi-Class Spectral Clustering (MCSC) and Hierarchical Agglomerative Clustering (HAC). The proposed method well fits the problem of blind image clustering because it does not a priori require the knowledge of the amount of classes in which the dataset has to be divided but it needs only a stop threshold; such a threshold has been properly defined by means of a ROC curves approach by relying on the goodness of cluster aggregation. Several experimental tests have been carried out in different operative conditions and the proposed methodology globally presents superior performances in terms of clustering accuracy and robustness as well as a reduced computational burden.

Blind image clustering based on the Normalized Cuts criterion for camera identification / Amerini, Irene; Caldelli, Roberto; Crescenzi, Pierluigi; DEL MASTIO, Andrea; Marino, Andrea. - In: SIGNAL PROCESSING-IMAGE COMMUNICATION. - ISSN 0923-5965. - 29:8(2014), pp. 831-843. [10.1016/j.image.2014.07.003]

Blind image clustering based on the Normalized Cuts criterion for camera identification

AMERINI, IRENE
;
CRESCENZI, PIERLUIGI;
2014

Abstract

Camera identification is a well known problem in image forensics, addressing the issue to identify the camera a digital image has been shot by. In this paper, we pose our attention to the task of clustering images, belonging to a heterogenous set, in groups coming from the same camera and of doing this in a blind manner; this means that side information neither about the sources nor, above all, about the number of expected clusters is requested. A novel methodology based on Normalized Cuts (NC) criterion is presented and evaluated in comparison with other state-of-the-art techniques, such as Multi-Class Spectral Clustering (MCSC) and Hierarchical Agglomerative Clustering (HAC). The proposed method well fits the problem of blind image clustering because it does not a priori require the knowledge of the amount of classes in which the dataset has to be divided but it needs only a stop threshold; such a threshold has been properly defined by means of a ROC curves approach by relying on the goodness of cluster aggregation. Several experimental tests have been carried out in different operative conditions and the proposed methodology globally presents superior performances in terms of clustering accuracy and robustness as well as a reduced computational burden.
2014
image forensics; source identification; clustering; normalized cuts
01 Pubblicazione su rivista::01a Articolo in rivista
Blind image clustering based on the Normalized Cuts criterion for camera identification / Amerini, Irene; Caldelli, Roberto; Crescenzi, Pierluigi; DEL MASTIO, Andrea; Marino, Andrea. - In: SIGNAL PROCESSING-IMAGE COMMUNICATION. - ISSN 0923-5965. - 29:8(2014), pp. 831-843. [10.1016/j.image.2014.07.003]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1325015
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