Green Steganalyzer: A Green Learning Approach to Image Steganalysis

TL;DR

Green Steganalyzer introduces a modular, low-complexity image steganalysis method based on green learning, achieving competitive detection accuracy with enhanced transparency and suitability for mobile applications.

Contribution

The paper presents a novel green learning-based framework for image steganalysis, combining anomaly prediction, embedding detection, and decision fusion, with improved efficiency and interpretability.

Findings

Achieves detection performance comparable to deep learning models.

Significantly lower computational complexity and smaller model size.

Suitable for mobile and edge device deployment.

Abstract

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.