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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ImageNet classification with deep convolutional neural networks
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Flagged first, then by citations
Research works printed but not linked: 4
- Cozzolino D., IEEE International Conference on Image Processing. 5302--5306
- Fan W., IEEE International Workshop on Information Forensics and Security. 1--6.
- Z. Lan S. Yu M. Lin B. Raj and A.G. Hauptmann. 2015. Local handcrafted features are convolutional neural networks. arXiv preprint arXiv:1511.05045 (2015). Z. Lan S. Yu M. Lin B. Raj and A.G. Hauptmann. 2015. Local handcrafted features are convolutional neural networks. arXiv preprint arXiv:1511.05045 (2015). · arXiv
- Verdoliva L., IEEE Workshop on Information Forensics and Security. 149--154
Cited by
Sorted by citations
Items 101–104 of 104
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
Zhu, H., Fu, C., Wu, Q., Wu, W., Qian, C., & He, R. (2020) · arXiv · doi:10.48550/arxiv.2011.02674 · Cited by 20
Visual-Semantic Transformer for Face Forgery Detection
Xu, Y., Jia, G., Huang, H., Duan, J., & He, R. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484407 · Cited by 24
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M., & Verdoliva, L. (2018) · arXiv · doi:10.48550/arxiv.1812.02510 · Cited by 221
Self-citationDeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.07634 · Cited by 280
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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