利用数据几何缓解隐写分析中的CSM问题
Leveraging Data Geometry to Mitigate CSM in Steganalysis
AI总结：
针对隐写分析中的载体源失配（CSM）问题，提出基于DCTr特征子空间弦距的几何度量，以此选择定制训练数据集，提升模型在目标场景的泛化性能，效果优于传统方法。
摘要：
在实际应用场景中，隐写术使用者使用的载体图像来自各类传感器与处理流水线，与研究人员训练隐写分析模型所用的载体存在显著差异。这导致模型处理分布外载体时会出现不可避免的性能差距，该问题通常被称为载体源失配（Cover Source Mismatch，CSM）。本研究针对测试图像经同一流水线处理、但标签信息以及载体与隐写图像的比例均未知的场景展开，目标是找到能最大程度泛化到目标场景的训练数据集。通过对一组会引发CSM的处理流水线网格进行探究，我们发现了一种基于DCTr特征张成的子空间之间弦距的几何度量指标，该指标与实际 regret 高度相关，且不受载体-隐写图像比例的影响。我们的贡献在于提出了一种策略，能够选择或生成定制化训练数据集，提升针对给定目标的整体泛化性能。实验验证表明，在合理假设下，我们的基于几何的优化策略优于传统的原子化方法。相关资源可访问github.com/RonyAbecidan/LeveragingGeometrytoMitigateCSM获取。
英文摘要：
In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train steganalysis models. This leads to an inevitable performance gap when dealing with out-of-distribution covers, commonly referred to as Cover Source Mismatch (CSM). In this study, we consider the scenario where test images are processed using the same pipeline. However, knowledge regarding both the labels and the balance between cover and stego is missing. Our objective is to identify a training dataset that allows for maximum generalization to our target. By exploring a grid of processing pipelines fostering CSM, we discovered a geometrical metric based on the chordal distance between subspaces spanned by DCTr features, that exhibits high correlation with operational regret while being not affected by the cover-stego balance. Our contribution lies in the development of a strategy that enables the selection or derivation of customized training datasets, enhancing the overall generalization performance for a given target. Experimental validation highlights that our geometry-based optimization strategy outperforms traditional atomistic methods given reasonable assumptions. Additional resources are available at github.com/RonyAbecidan/LeveragingGeometrytoMitigateCSM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
