Steganographic Tool Detection using Specific Composite Feature Set and Weighted Decision Function
S. Arivazhagan1, W. Sylvia Lilly Jebarani2, S. T. Veena3
1S. Arivazhagan, Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
2W. Sylvia Lilly Jebarani, Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
3S. T. Veena, Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
Manuscript received on 19 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 612-618 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11130782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1113.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Steganographic tools available in the internet and other commercial steganographic tools are preferred than customized steganographic tools developed from scratch by unlawful groups. Hence a clue regarding the steganographic tool deployed in the covert communication process can save time for the steganalyst in the crucial active steganalysis phase. Signature analysis can lead to success in targeted steganalysis but tool detection needs to be taken forward from a point with a suspicious stego image in hand with no additional details available. In such scenarios, statistical steganalysis comes to rescue but with issues to be addressed like huge dimensionality of feature sets and complex ensemble classifiers. This work accomplishes tool detection with a specific composite feature set identified to distinguish one stego tool from the others with a weighted decision function to enhance the role of the specific feature set when it votes for a particular class. A tool detection accuracy of 85.25% has been achieved simultaneously addressing feature set dimensionality and complexity of ensemble classifiers and a comparison with a benchmark procedure has been made. Keywords—Steganographic tool detection, specific composite feature set, weighted decision function, ensemble classification; feature/colour model/domain/significant function selection.
Keywords: Steganographic Tool Detection, Specific Composite Feature Set, Weighted Decision Function, Ensemble Classification; Feature/Colour Model/Domain/Significant Function Selection.
Scope of the Article: Encryption Methods and Tools