Towards Open Set Camera Model Identification Using a Deep Learning Framework

Bayar, Belhassen; Stamm, Matthew C.

doi:10.1109/icassp.2018.8462383

Cited by 32 publications

(48 citation statements)

References 15 publications

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“…This is basically a two-class classification problem that does not provide the analyst with information on the actual used camera model. To infer the possible used camera model, an open-set detection solution should be paired with a subsequent step of closed-set classification, as proposed by Bayar and Stamm [21].…”

Section: B Open-set Detectionmentioning

confidence: 99%

“…Previous works in open-set camera model identification have not fully evaluated the multiclass open-set classification problem. Bayar and Stamm [21] have considered the performance of the classification methods for detecting known vs unknown and, independently, the closed-set classification performance among the classes. In this latter evaluation, VOLUME 0, 2019 the classifiers work in a closed-set scenario, i.e., they never predicts as unknown.…”

Section: Open-set Classificationmentioning

confidence: 99%

“…3) Normalized Accuracy (NA) [23] This is the average between AKS and AUS and provides an overall view of a classifier performance in terms of both open-and closed-set scenarios. 4) Detection Accuracy (DA) [21] This averages the percentage of images from known cameras detected as coming from known models, and the percentage of images from unknown cameras detected as coming from unknown models. This metric does not take into account whether images from known cameras are misclassified to the wrong camera model.…”

Section: B Metricsmentioning

confidence: 99%

“…To the best of our knowledge, the only work presenting results for the open-set camera model identification problem is the work of Bayar and Stamm [21]. In particular, in this work, the authors propose two different approaches.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

“…The first approach proposed by Bayar and Stamm [21] works as it follows. A multi-class classifier is trained with Closed training protocol.…”

Section: ) Approachmentioning

confidence: 99%

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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: B Open-set Detectionmentioning

confidence: 99%

Section: Open-set Classificationmentioning

confidence: 99%

Section: B Metricsmentioning

confidence: 99%

Section: Comparison With State-of-the-artmentioning

confidence: 99%

“…The first approach proposed by Bayar and Stamm [21] works as it follows. A multi-class classifier is trained with Closed training protocol.…”

Section: ) Approachmentioning

confidence: 99%

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

et al. 2019

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MultiMedia Modeling

2019

Lecture Notes in Computer Science

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The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

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