Performance Evaluation of Steganography Tools Using SVM and NPR Tool

Steganography is the art of hiding the secret messages in an innocent medium like images, audio, video, text, etc. such that the existence of any secret message is not revealed. There are various Steganography tools available. In this paper, we are considering three algorithms - nsF5, PQ,Outguess. To compare the robustness and to withstand the steganalytic attack of the above three algorithms, an algorithm based on sensitive features is presented. SVM and Neural Network Pattern Recognition Tool is used on sensitive features extracted from DCT domain. A comparison between the accuracy obtained from SVM and NPR is also shown. Experimental results show that the Outguess method can withstand steganalytic attack by a margin of 35% accuracy as compared to nsF5 and PQ, hence Outguess is more reliable for Steganography.