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.

翻译：本文提出了一种基于绿色学习范式的新型图像隐写分析解决方案——绿色隐写分析器（GS）。GS包含三个模块：1）基于像素的异常预测，2）嵌入位置检测，以及3）图像级检测的决策融合。在第一个模块中，GS将图像分解成图像块，采用Saab变换进行特征提取，并通过自监督学习预测其中心像素的异常分数。在第二个模块中，GS分析像素及其邻域的异常分数，以找到具有更高嵌入概率的像素。在第三个模块中，GS聚焦于更高嵌入概率的像素，融合其异常分数以作出最终的图像级分类。与最先进的深度学习模型相比，GS在针对S-UNIWARD、WOW和HILL隐写方案时实现了相当的检测性能，同时计算复杂度显著降低、模型尺寸更小，使其在移动/边缘应用中具有吸引力。此外，GS因其模块化设计而具有数学透明性。