Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6941556 | Signal Processing: Image Communication | 2018 | 25 Pages |
Abstract
Camera model identification is a fundamental task for many investigative activities, and is drawing great attention in the research community. In this context, convolutional neural networks (CNN) are expected to provide a significant performance gain over the current state of the art, as already happened for a wide range of image processing applications. However, recent studies enlightened the vulnerability of CNNs to adversarial attacks, casting shadows on their reliability for critical applications. In this paper, we investigate the robustness to adversarial attacks of CNN-based methods for camera model identification. Several networks and attack methods are considered, both when the attacker has complete knowledge of the network and when only the training set is available. In addition, the analysis concerns both original and JPEG compressed images, to simulate a social network environment. The experiments, carried out on a publicly available dataset with images coming from 29 different camera models, shed some light on the suitability of CNN-based approaches for this task.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
F. Marra, D. Gragnaniello, L. Verdoliva,