The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Median Filtering Forensics Based on Convolutional Neural Networks
Abstract
Median filtering detection has recently drawn much attention in image editing and image anti-forensic techniques. Current image median filtering forensics algorithms mainly extract features manually. To deal with the challenge of detecting median filtering from small-size and compressed image blocks, by taking into account of the properties of median filtering, we propose a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image. To our best knowledge, this is the first work of applying CNNs in median filtering image forensics. Unlike conventional CNN models, the first layer of our CNN framework is a filter layer that accepts an image as the input and outputs its median filtering residual (MFR). Then, via alternating convolutional layers and pooling layers to learn hierarchical representations, we obtain multiple features for further classification. We test the proposed method on several experiments. The results show that the proposed method achieves significant performance improvements, especially in the cut-and-paste forgery detection.
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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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,503
”Break Our Steganographic System”: The Ins and Outs of Organizing BOSS
Bas, P., Filler, T., & Pevný, T. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24178-9_5 · Cited by 1,250
GradientBased Learning Applied to Document Recognition
(2009) · Intelligent Signal Processing · doi:10.1109/9780470544976.ch9 · Cited by 1,135
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,095
UCID: an uncompressed color image database:
Schaefer, G., & Stich, M. (2003) · SPIE Proceedings · doi:10.1117/12.525375 · Cited by 1,071
Learning mid-level features for recognition
Boureau, Y.-L., Bach, F., LeCun, Y., & Ponce, J. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539963 · Cited by 1,052
Cited by
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer
Bayar, B., & Stamm, M. C. (2016) · Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/2909827.2930786 · Cited by 838
Two-Stream Neural Networks for Tampered Face Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.229 · Cited by 700
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy-Chowdhury, A. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2895466 · Cited by 477
Fake face detection via adaptive manipulation traces extraction network
Guo, Z., Yang, G., Chen, J., & Sun, X. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103170 · Cited by 165
Self-citationA detailed analysis of image and video forgery detection techniques
Tyagi, S., & Yadav, D. (2023) · The Visual Computer · doi:10.1007/s00371-021-02347-4 · Cited by 109
Metadata
- Authors
- Chen, J., Kang, X., Liu, Y., & Wang, Z. J.
- Venue
- IEEE Signal Processing Letters
- Published
- 2015-11
- Volume
- 22
- Issue
- 11
- Pages
- 1849-1853
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2407561938
- Cited by
- 425
- References
- 21
- FWCI
- 28.59
- Type
- journal article
- Language
- en
- Open access
- closed
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