Toward Real-world Text Image Forgery Localization: Structured and Interpretable Data Synthesis
Zeqin Yu, Haotao Xie, Jian Zhang, Jiangqun Ni, Wenkang Su, Jiwu Huang
Existing Text Image Forgery Localization (T-IFL) methods often suffer from poor generalization due to the limited scale of real-world datasets and the distribution gap caused by synthetic data that fails to capture real-world tampering complexity. To tackle this issue, we propose Fourier Series-based Tampering Synthesis (FSTS), a structured and interpretable framework for synthesizing tampered text images. TFSTS first collects 16,750 real-world tampering instances from five representative manipulation types, using a structured pipeline that records human-performed editing traces via multi-format logs (e.g., video, PSD, and editing logs). By analyzing these collected parameters and identify recurring behavioral patterns at both individual and population levels, we formulate a hierarchical modeling framework, where each individual’s tampering parameter is represented as a compact combination of basis operation–parameter configurations, and the population-level distribution is constructed by aggregating these behaviors. Since this formulation draws inspiration from Fourier series, it enable an interpretable approximation using basis functions and their learned weights. By sampling from this modeled distribution, FSTS synthesizes diverse and realistic training data that better reflect real-world forgery traces. Extensive experiments across four evaluation protocols demonstrate that models trained with FSTS data achieve significantly improved generalization on real-world datasets.