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

Abstract

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.

Media forensics on social media platforms: a survey

“…This method exploits batch partitioning, image resizing, hierarchical and graph-based clustering to group the images which results in more precise clusters for images taken with the same smartphone model. In [13] and [21], the camera model identification issue in an open set scenario with limited knowledge is addressed: the aim in this case is to detect whether an image comes from one of the known camera models of the dataset or from an unknown one.…”

“…VISION [15] is the most widely employed dataset for the source camera identification problem in a whole, and is also used for platform provenance tests. The FLICKR UNICAMP [13] and SDRG [22] datasets have been also proposed with regards to perfect knowledge methods and to limited and zero knowledge methods respectively. Comprehensive datasets to support various forensics evaluation tasks are the Media Forensics Challenge (MFC) [80] dataset with 35 million internet images and 300,000 video clips and the Fake Video Corpus (FVC) [81] that exploits three different social networks.…”

A Metadata-Based Approach for Research Discipline Prediction Using Machine Learning Techniques and Distance Metrics

“…A typical approach to compare our research findings to related work is by evaluating the performance of the proposed machine learning models based on the commonly used evaluation metrics such as accuracy, precision, recall, and F1-score on various datasets. This approach is widely adopted in almost every research in the area of machine learning models design [45], [46], [47], [48]. In a similar manner, we compared the proposed approach to related work by applying them to the same dataset and using the same metric to evaluate their performance.…”