Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

CIF: A Constrained Inversion Framework for Reliable Message Extraction in Diffusion-Based Generative Steganography

Yuqi Qian, Yun Cao, Meiyang Lv, Haocheng Fu

TL;DR

This work addresses unreliable message extraction in diffusion-based generative steganography by diagnosing two core inversion errors: dynamic structural error and numerical integration error. It introduces CIF, a constrained inversion framework with Path Consistency Inversion (PCI) to enforce linear, velocity-aligned trajectories and Adaptive Hybrid-Order Solver (AHOS) to adapt integration order to local stability. Empirical results show CIF reduces latent reconstruction error by over 35% and achieves top extraction accuracy under various distortions, including JPEG compression and resizing, while maintaining security against steganalysis and a high hiding capacity. The approach offers practical, robust, end-to-end extraction reliability for high-capacity steganography in real-world transmission scenarios.

Abstract

Generative image steganography aims to conceal secret information in generated images without arousing suspicion. However, in practical scenarios involving high-capacity embedding or lossy transmission, existing methods still suffer from limited extraction accuracy. The main challenge lies in accurately recovering the secret-embedded latent vectors from stego images. To address this issue, we propose CIF, a constrained inversion framework designed to achieve accurate message extraction. Specifically, CIF reduces dynamic structural errors by enforcing linear consistency in the latent space, meanwhile reduces numerical integration errors by adaptively adjusting the integration order according to local trajectory stability. Experimental results show that our method reduces latent reconstruction error by more than 35\% and achieves higher message extraction accuracy than existing approaches.

CIF: A Constrained Inversion Framework for Reliable Message Extraction in Diffusion-Based Generative Steganography

TL;DR

This work addresses unreliable message extraction in diffusion-based generative steganography by diagnosing two core inversion errors: dynamic structural error and numerical integration error. It introduces CIF, a constrained inversion framework with Path Consistency Inversion (PCI) to enforce linear, velocity-aligned trajectories and Adaptive Hybrid-Order Solver (AHOS) to adapt integration order to local stability. Empirical results show CIF reduces latent reconstruction error by over 35% and achieves top extraction accuracy under various distortions, including JPEG compression and resizing, while maintaining security against steganalysis and a high hiding capacity. The approach offers practical, robust, end-to-end extraction reliability for high-capacity steganography in real-world transmission scenarios.

Abstract

Generative image steganography aims to conceal secret information in generated images without arousing suspicion. However, in practical scenarios involving high-capacity embedding or lossy transmission, existing methods still suffer from limited extraction accuracy. The main challenge lies in accurately recovering the secret-embedded latent vectors from stego images. To address this issue, we propose CIF, a constrained inversion framework designed to achieve accurate message extraction. Specifically, CIF reduces dynamic structural errors by enforcing linear consistency in the latent space, meanwhile reduces numerical integration errors by adaptively adjusting the integration order according to local trajectory stability. Experimental results show that our method reduces latent reconstruction error by more than 35\% and achieves higher message extraction accuracy than existing approaches.

Paper Structure

This paper contains 26 sections, 14 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminary and Related Work
  3. Diffusion-based Generative Image Steganography
  4. Forward and Reverse Dynamics in Diffusion Models
  5. Methodology
  6. Problem Analysis
  7. Dynamic Structural Error
  8. Numerical Integration Error
  9. The Overview of CIF
  10. Path Consistency Inversion
  11. Theoretical Formulation
  12. Practical Realization
  13. Adaptive Hybrid-Order Solver
  14. Motivation: Temporal Inconsistency in Diffusion Inversion
  15. Adaptive Integration Mechanism
  16. ...and 11 more sections

Figures (3)

  • Figure 1: The process of diffusion-based generative image steganography schemes and proposed CIF.
  • Figure 2: Architecture of the proposed Constrained Inversion Framework (CIF) combining PCI and AHOS modules. Path Consistency Inversion (PCI) enforces geometric alignment of forward and reverse trajectories, while the Adaptive Hybrid-Order Solver (AHOS) adaptively regulates numerical precision. Together they ensure accurate and efficient recovery of hidden information.
  • Figure 3: Temporal distribution of trajectory consistency in diffusion inversion.