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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
2013 IEEE International Conference on Computer Vision
(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,232
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144
NetVLAD: CNN architecture for weakly supervised place recognitionPreprint
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2015) · arXiv (Cornell University) · Cited by 1,790
Gray, R. M., & Neuhoff, D. L. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720541 · Cited by 1,647
Steganalysis by Subtractive Pixel Adjacency Matrix
Pevný, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 899
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
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314
Self-citationDeepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058
Multi-attentional Deepfake Detection
Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
