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Practical applications of digital forensics are often faced with the challenge of grouping large-scale suspicious images into a vast number of clusters, each containing images taken by the same camera. This task can be approached by resorting to the use of sensor pattern noise (SPN), which serves as the fingerprint of the camera. The challenges of large-scale image clustering come from the sheer volume...
Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable,...
We present in this work an unsupervised image classifier, which is capable of clustering images taken by an unknown number of unknown digital cameras into a number of classes, each corresponding to one camera. The classification system first extracts and enhances a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. Secondly, it applies...
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