A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images

Abstract

The detection of stego images by using the steganalysis approach is one of the demanding task in the recent days. Because, it is used as an importer for the immoral activities by hiding the secrets in the messages. For this secret identification, the traditional works develop various steganographic techniques for steganalysis. But, it has some important drawbacks such as low detection percentage, inefficient results, and increased complexity. In order to solve these issues, this paper introduces a new steganalysis approach with an efficient feature selection and optimization techniques. The aim of this paper is to accurately detect the stego and clean images by implementing an efficient classification algorithm. Initially, a novel Coefficient based Walsh Hadamard Transform along with the Gray Level Co-occurrence Matrix (GLCM) is used for extracting the features of the image. Then, an efficient feature selection technique, namely, Pine Growth Optimization (PGO) is developed to select the optimal features from the extracted features. Finally, the Cross Integrated Machine Learning (CIML) classifier is implemented to classify the stego and clean images. The newness is provided during the feature extraction, selection and classification processes. In experiments, the performance results of the proposed steganalysis is evaluated and compared with the existing approaches by using different measures.