A Low Cost Image Steganalysis by Using Domain Adaptation

Information hiding and data encryption are used widely to protect data and information from anonymous access. In digital world, hiding and encrypting of the desired data into an image is a smart way to protect information with a low cost. In the digital images, steganalysis is a known method to distinguish between clean and stego images. Most of recent researches in this scope exploit feature reduction algorithms to improve the performance of correct detections. However, dimension reduction alone could not tackle the problem of steganalysis because the properties of stego images change during the steganalysis process. In this work, it is intended to propose an Image Steganalysis using visual Domain Adaptation (ISDA), which this steganalysis target images to distinguish across stego and clean images. ISDA is a dimensionality reduction approach that considers the image drifts during the steganography process in the steganalysis of target images. Moreover, ISDA employs domain invariant clustering in an embedded representation to cluster clean and stego images in the reduced subspace. The results on benchmark datasets demonstrate that ISDA thoroughly outperforms all of the state of the art methods on validation parameters, accuracy of detection and time complexity.