The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Abstract
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.
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Metadata
- Authors
- Cozzolino, D., Poggi, G., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- Venue
- Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security
- Publisher
- ACM
- Published
- 2017-06-20
- Pages
- 159-164
- OpenAlex
- W2603123944
- Cited by
- 349
- References
- 30
- Citations by year
- 2026: 12 · 2025: 39 · 2024: 32 · 2023: 53 · 2022: 67 · 2021: 61 · 2020: 50 · 2019: 17 · 2018: 16 · 2017: 2
- FWCI
- 6.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) (publishedVersion)
- Funders
- Defense Advanced Re search Projects Agency (FA8750-16-2-0204)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- fine-tuning, image forgery detection, image localization, forensic image analysis
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