SVM-Based Image Steganalysis Algorithm with Three-Domain Feature Fusion

This paper presents an SVM-based image steganalysis algorithm that incorporates feature fusion across three distinct domains: DCT (Discrete Cosine Transform) domain, DWT (Discrete Wavelet Transform) domain, and spatial domain. The algorithm aims to enhance the accuracy and robustness of image steganalysis by fusing features extracted from multiple domains. In implementation, the algorithm first employs an SVM classifier for initial image categorization. Key feature extraction functions are then applied to derive domain-specific characteristics: DCT coefficients are computed using block-based transformation, wavelet subbands are analyzed through multi-level DWT decomposition, and spatial features are extracted directly from pixel values. The fusion process typically involves feature concatenation or weighted combination methods in the code, creating a comprehensive feature vector. Finally, this fused feature vector is utilized for advanced steganalysis to detect potential hidden information. The implementation may include dimensionality reduction techniques like PCA before SVM classification to optimize performance. This multi-domain approach significantly improves the detection capability for steganographic content, thereby enhancing overall information security.