Frequency-Enhanced Multi-Scale Progressive Detection for Document Image Manipulation

Images playa vital role in modern communication, cultural exchange, and information dissemination. Document images, as a digital form of textual content, are widely used in government, finance, and e-commerce scenarios. However, tampered with document images has become increasingly common and is often exploited for identity fraud and financial scams, posing serious threats to platform credibility and public interests. Compared to natural images, document forgeries typically involve small, visually inconspicuous regions against uniform backgrounds, making RGB-based detection methods less effective. To address these challenges, we propose MFPD-Net (Multi-scale Frequency Progressive Detector), which integrates a Transformer-based frequency feature enhancement module combined with an adaptive feature fusion strategy. Additionally, a Progressive multi-scale feedback decoder is designed to refine mask generation and improve localization accuracy. Extensive experiments on multiple document tampered detection tasks demonstrate that our method outperforms existing approaches in both accuracy and robustness, showing strong practicality and generalization capabilities.