Blind Feature-Based Steganalysis with and Without Cross Validation on Calibrated JPEG Images Using Support Vector Machine
| dc.contributor.author | D. Shankar,Deepa | |
| dc.contributor.author | Azhakath ,Adresya Suresh | |
| dc.date.accessioned | 2024-06-06T06:41:25Z | |
| dc.date.available | 2024-06-06T06:41:25Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The paper presents the comparative result analysis of calibrated JPEG images with and without cross-validation technique. Pixel-value differencing, LSB replacement, F5 and LSB Matching are used as steganographic algorithms. 25% of embedding is considered for the analysis. The images are calibrated before they are considered for analysis and relevant features are extracted. The classifier used is SVM with six various kernels and four types of sampling methods. The sampling methods are linear, shuffle, stratified and automatic. Radial, dot, Epanechnikov, multiquadratic, polynomial and ANOVA kernels are taken into consideration in this paper. Keywords: LSB Matching, Epanechnikov,, Multiquadratic, Polynomial. | en |
| dc.identifier.citation | Shankar, D. D., & Azhakath, A. S. (2020). Blind Feature-Based Steganalysis with and Without Cross Validation on Calibrated JPEG Images Using Support Vector Machine. In Innovation in Electrical Power Engineering, Communication, and Computing Technology: Proceedings of IEPCCT 2019 (pp. 17-27). Springer Singapore. | |
| dc.identifier.doi | http://dx.doi.org/10.1007/978-981-15-2305-2_2 | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/5717 | |
| dc.language.iso | en | |
| dc.publisher | Springer link | |
| dc.title | Blind Feature-Based Steganalysis with and Without Cross Validation on Calibrated JPEG Images Using Support Vector Machine | |
| dc.type | Animation |