Conference proceeding
Learning Unified Deep-Features for Multiple Forensic Tasks
PROCEEDINGS OF THE 6TH ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY (IH&MMSEC'18), pp 79-84
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Recently, deep learning researchers have developed a technique known as deep features in which feature extractors for a task are learned by a CNN. These features are then provided to another classifier, or even used to perform a different classification task. Research in deep learning suggests that in some cases, deep features generalize to seemingly unrelated tasks. In this paper, we develop techniques for learning deep features that can be used across multiple forensic tasks, namely image manipulation detection and camera model identification. To do this, we develop two approaches for building deep forensic features: a transfer learning approach and a multitask learning approach. We experimentally evaluate the performance of both approaches in several scenarios and find that: 1) features learned for camera model identification generalize well to manipulation detection tasks but manipulation detection features do not generalize well to camera model identification, suggesting a task asymmetry, 2) deeper features are more task specific while shallower features generalize well across tasks, suggesting a feature hierarchy, and 3) a single, unified feature extractor can be learned that is highly discriminative for multiple forensic tasks. Furthermore, we find that when there is limited training data, a unified feature extractor can significantly outperform a targeted CNN.
Metrics
Details
- Title
- Learning Unified Deep-Features for Multiple Forensic Tasks
- Creators
- Owen Mayer - Drexel UniversityBelhassen Bayar - Drexel UniversityMatthew C. Stamm - Drexel UniversityACM
- Publication Details
- PROCEEDINGS OF THE 6TH ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY (IH&MMSEC'18), pp 79-84
- Publisher
- Assoc Computing Machinery
- Number of pages
- 6
- Grant note
- 1553610 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000475951600012
- Scopus ID
- 2-s2.0-85050462131
- Other Identifier
- 991019167996704721
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- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic