Universal blind steganalysis via reference points-based local outlier factor
Xiaodan Hou, Tao Zhang · 2017
We consider a particular paradigm of steganalysis, called universal blind steganalysis, namely, no knowledge of the steganographic way and all knowledge of the cover-source. Its goal is to detect all known (already existing) and unknown (previously unseen) steganographic algorithms in such a paradigm. However, the existing approaches can not achieve overall good performance on both known and unknown stego algorithms. Motivated by these observations, we explore a simple and effective approach for construction of universal blind steganalyzer. First, we compute local outlier factor (LOF) scores of known stego sample points (feature vectors) with respect to the test sample points. Then, we choose stego images with the lowest LOF scores from known stego images as training stego images. Finally, we train a binary classifier on cover images and chosen training stego images for test. Experimental results confirm that the proposed approach performs significantly better than three state-of-the-art approaches on both known and unknown stego algorithms.