🤖 AI Summary
To address the vulnerability of existing 3D mesh steganography methods to steganalyzers due to geometric distortions, this paper proposes a Feature-Preserving Distortion (FPD)-driven adaptive embedding framework. First, we introduce the first differentiable, weighted distortion metric specifically designed for 3D meshes, jointly optimizing geometric fidelity and steganalyzer robustness. Second, we design a Q-layered syndrome-trellis code (STC) mechanism that automatically computes bit-modification probabilities in a distortion-aware manner, enabling adaptive embedding optimization. Third, we incorporate multi-scale 3D steganalytic sub-features to guide distortion modeling. Experiments demonstrate that our method achieves state-of-the-art (SOTA) detection resistance against mainstream 3D steganalyzers, while maintaining high embedding capacity (>1.5 bpp) and significantly improved geometric fidelity—reducing Hausdorff distance by 42% compared to conventional geometry-perturbation-based approaches.
📝 Abstract
Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math><alternatives><mml:math><mml:mi>Q</mml:mi></mml:math><inline-graphic xlink:href="zhu-ieq1-3289234.gif"/></alternatives></inline-formula>-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math><alternatives><mml:math><mml:mi>Q</mml:mi></mml:math><inline-graphic xlink:href="zhu-ieq2-3289234.gif"/></alternatives></inline-formula>-layered STC, given the variation of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math><alternatives><mml:math><mml:mi>Q</mml:mi></mml:math><inline-graphic xlink:href="zhu-ieq3-3289234.gif"/></alternatives></inline-formula>, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.
