Multiperspective Progressive Structure Adaptation for JPEG Steganography Detection Across Domains.

Area of Science:

• Digital Forensics
• Computer Vision
• Information Security

Background:

• Steganography detection aims to identify hidden data in multimedia.
• Cross-domain detection is challenging due to inconsistent data distributions.
• Existing methods struggle to exploit transferable correlation information effectively.

Purpose of the Study:

• To present a novel multiperspective progressive structure adaptation (MPSA) scheme for JPEG steganography detection across domains.
• To address the challenge of exploiting transferable correlation information in cross-domain steganalysis.
• To improve the accuracy and robustness of steganography detection in mismatched domains.

Main Methods:

• Clustering source and target data to explore intradomain and interdomain structures.
• Utilizing active progressive learning (APL) to reduce nonlinear distribution discrepancy via structure vectors.
• Employing iterative optimization with constraints for structure adaptation across multiple domains.

Main Results:

• The proposed MPSA scheme effectively captures global and local modalities, improving the signal-to-noise ratio of weak stego signals.
• Structure adaptation enhances knowledge discrimination and transferability across domains.
• The unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) prevents negative transfer.

Conclusions:

• The MPSA scheme demonstrates superior performance over state-of-the-art methods in benchmark cross-domain steganography detection tasks.
• The approach offers a robust solution for detecting hidden information in JPEG images across diverse domains.
• This work advances the field of cross-domain steganalysis by enabling more effective transfer of detection knowledge.