Blind image steganalysis based on evidential K-Nearest Neighbors

Blind steganalysis techniques are able to detect the presence of secret messages embedded in digital media files, such as images, video, and audio, with an unknown steganography algorithm. This paper present an image steganalysis method based on Evidential K-Nearest Neighbors (EV-knn). Originality of this work is the use of theoretical framework of Belief functions on different subspaces of features vectors. Classifications obtained in subspaces are combined using specific combination function and to provide classification of a given image (cover or stego). The proposed approach is evaluated with the classical nsf5 steganographic method that hides messages in JPEG images. Compared to Ensemble Classifier steganalysis algorithm, the proposed approach significantly increases the performance of classification.