Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature
| Autor: | Yuanyuan Ma, Ruixia Jin, Xintao Duan, Yihao Wang, Tao Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Steganalysis
information gain ratio Physics and Astronomy (miscellaneous) Steganography Basis (linear algebra) business.industry Computer science steganalysis feature components General Mathematics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Feature selection contribution degree distortion factor Feature Dimension feature selection Dimension (vector space) Chemistry (miscellaneous) Feature (computer vision) Computer Science (miscellaneous) Information gain ratio QA1-939 Artificial intelligence business Mathematics |
| Zdroj: | Symmetry, Vol 13, Iss 1775, p 1775 (2021) Symmetry Volume 13 Issue 10 |
| ISSN: | 2073-8994 |
| Popis: | Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which provides the theoretical basis for selecting the feature components that contribute to a great degree to detecting the stego images. Additionally, the feature components are arranged in descending order rely on the two measurement criteria, which provides the basis for deleting the components. Based on the above, removing feature components that are ranked larger differently according to two criteria. Ultimately, the preserved feature components are used as the final selected feature for training and detection. Comparison experiments with existing classical approaches indicate that this approach can effectively reduce the feature dimension while maintaining or even improving the detection accuracy. At the same time, it can reduce the detection spatio-temporal complexity of the stego images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|