Green Steganalyzer: A Green Learning Approach to Image Steganalysis
Yao Zhu Xinyu Wang Hong-Shuo Chen Ronald Salloum C. -C. Jay Kuo
A novel learning solution to image steganalysis based on the green learning
paradigm, called Green Steganalyzer (GS), is proposed in this work. GS consists
of three modules: 1) pixel-based anomaly prediction, 2) embedding location
detection, and 3) decision fusion for image-level detection. In the first
module, GS decomposes an image into patches, adopts Saab transforms for feature
extraction, and conducts self-supervised learning to predict an anomaly score
of their center pixel. In the second module, GS analyzes the anomaly scores of
a pixel and its neighborhood to find pixels of higher embedding probabilities.
In the third module, GS focuses on pixels of higher embedding probabilities and
fuses their anomaly scores to make final image-level classification. Compared
with state-of-the-art deep-learning models, GS achieves comparable detection
performance against S-UNIWARD, WOW and HILL steganography schemes with
significantly lower computational complexity and a smaller model size, making
it attractive for mobile/edge applications. Furthermore, GS is mathematically
transparent because of its modular design.