The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Random Projections of Residuals for Digital Image Steganalysis
Abstract
The traditional way to represent digital images for feature based steganalysis is to compute a noise residual from the image using a pixel predictor and then form the feature as a sample joint probability distribution of neighboring quantized residual samples-the so - called co-occurrence matrix. In this paper, we propose an alternative statistical representation - instead of forming the co-occurrence matrix, we project neighboring residual samples onto a set of random vectors and take the first-order statistic (histogram) of the projections as the feature. When multiple residuals are used, this representation is called the projection spatial rich model (PSRM). On selected modern steganographic algorithms embedding in the spatial, JPEG, and side-informed JPEG domains, we demonstrate that the PSRM can achieve a more accurate detection as well as a substantially improved performance versus dimensionality trade-off than state-of-the-art feature sets.
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Citations
Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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
Self-citation”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
Ensemble Classifiers for Steganalysis of Digital Media
Kodovsky, J., Fridrich, J., & Holub, V. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2175919 · Cited by 1,064
Self-citationUsing High-Dimensional Image Models to Perform Highly Undetectable Steganography
Pevný, T., Filler, T., & Bas, P. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-16435-4_13 · Cited by 1,009
Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes
Filler, T., Judas, J., & Fridrich, J. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2134094 · Cited by 966
Self-citationDesigning steganographic distortion using directional filters
Holub, V., & Fridrich, J. (2012) · 2012 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2012.6412655 · Cited by 966
Self-citationSteganalysis by Subtractive Pixel Adjacency Matrix
Pevny, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 887
Self-citationSteganalysis by subtractive pixel adjacency matrix
Pevný, T., Bas, P., & Fridrich, J. (2009) · Proceedings of the 11th ACM workshop on Multimedia and security · doi:10.1145/1597817.1597831 · Cited by 769
Self-citationSteganalysis using image quality metrics
Avcibas, I., Memon, N., & Sankur, B. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.807363 · Cited by 562
Cited by
Deep learning for steganalysis via convolutional neural networks
Qian, Y., Dong, J., Wang, W., & Tan, T. (2015) · SPIE Proceedings · doi:10.1117/12.2083479 · Cited by 537
Deep residual learning for image steganalysis
Wu, S., Zhong, S., & Liu, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4440-4 · Cited by 442
Zhang, R., Zhu, F., Liu, J., & Liu, G. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2936913 · Cited by 420
Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters
Song, X., Liu, F., Yang, C., Luo, X., & Zhang, Y. (2015) · Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/2756601.2756608 · Cited by 300
DeepMIH: Deep Invertible Network for Multiple Image Hiding
Guan, Z., Jing, J., Deng, X., Xu, M., Jiang, L., Zhang, Z., & Li, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3141725 · Cited by 267
Metadata
- Authors
- Holub, V., & Fridrich, J.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2013-12
- Volume
- 8
- Issue
- 12
- Pages
- 1996-2006
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2040299224
- Cited by
- 261
- References
- 30
- FWCI
- 22.30
- Type
- journal article
- Language
- en
- Open access
- closed
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