The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Deep learning for steganalysis via convolutional neural networks
Abstract
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
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Cites
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Cited by
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499
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
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464
“similarity between real images and fake images and requires the forensic classifier to learn more intrinsic and meaningful features, which has better generalization capability than previous image forensics methods [61], [62] or image steganalysis networks [63].”
BackgroundDeep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Metadata
- Authors
- Qian, Y., Dong, J., Wang, W., & Tan, T.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2015-03-04
- Volume
- 9409
- Pages
- 94090J
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2046180645
- Cited by
- 537
- References
- 24
- FWCI
- 13.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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