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Self-attention enhanced deep residual network for spatial image steganalysis.
- Source :
-
Digital Signal Processing . Jul2023, Vol. 139, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- As a specially designed tool and technique for the detection of image steganography, image steganalysis conceals information under the carriers for covert communications. Being developed on the BOSSbase dataset and released a decade ago, most of the Convolutional Neural Network (CNN) architectures for spatial image steganalysis fail to achieve satisfactory performance on new challenging datasets, i.e. ALASKA#2, which was released recently and is more complex yet consistent with the real scenarios. In this paper, we propose an enhanced residual network (ERANet) with self-attention ability, which utilizes a more complex residual method and a global self-attention technique, to alleviate the problem. Compared to the residual network that was widely used in the state-of-the-art, the enhanced residual network mathematically employed a more sophisticated way to extract more effective features in the images and hence it is suitable for more complex situations in the new dataset. Our proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets at various sizes have demonstrated the effectiveness of the proposed methodology. In short, ERANet provides an improvement of about 3.77% on average, compared to a few state-of-the-art CNNs. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CONVOLUTIONAL neural networks
*CRYPTOGRAPHY
Subjects
Details
- Language :
- English
- ISSN :
- 10512004
- Volume :
- 139
- Database :
- Academic Search Index
- Journal :
- Digital Signal Processing
- Publication Type :
- Periodical
- Accession number :
- 164020506
- Full Text :
- https://doi.org/10.1016/j.dsp.2023.104063