Open-set recognition for different classifiers

Júnior, Pedro Ribeiro Mendes

doi:10.47749/t/unicamp.2018.1062988

Cited by 1 publication

(2 citation statements)

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“…Our results have shown that appropriate means of dealing with the open-set camera model attribution problem should be sought in order to properly handling the problem, considering that a recently proposed open-set method [22], as is, obtains considerable improved results compared to the straightforward idea of thresholding the softmax probability of neural networks for rejection as unknown (Section V-D1). This problem on thresholding the softmax probability for open-set recognition have been evinced in one of our previous work, hence the current work also confirms the previously more theoretical perspective [35,Chapter 7].…”

Section: Combinationsupporting

confidence: 87%

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An In-Depth Study on Open-Set Camera Model Identification

et al. 2019

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Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed in the literature. One of their main drawbacks, however, is the typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during investigation, i.e., training time. The fact that a picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. To deal with this issue, in this paper, we present an in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers specially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple alternative among the selected ones obtains the best results. Thorough testing on independent datasets shows that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even on small-scale image patches, improving over state-of-the-art available solutions. INDEX TERMS Camera model identification, image forensics, open-set recognition, open-set training protocol.

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Section: Combinationsupporting

confidence: 87%

“…Additionally, other methods derived from SVM have been proposed in the literature specifically for open-set problems. In this work, we considered the Weibull-calibrated SVM (WSVM) [32], Decision Boundary Carving (DBC) [33,34], Specialized Support Vector Machines (SSVM) [35], and SVM with Probability of Inclusion (PISVM) [22].…”

Section: Open-set Classifiersmentioning

confidence: 99%