Document Image Verification Based on Paragraph Alignment and Subtle Change Detection

The digitization of paper documents enables rapid sharing and long-term preservation of information, making it a widely adopted approach for efficient document storage and management across various domains. However, the recent advances in image editing software pose an increasing threat to the integrity of document images. Comparing the input with the corresponding reference document image is a direct and effective approach to verification. Nevertheless, this task is challenging due to two key factors, namely, the need for efficient retrieval of the reference document images and the difficulty of detecting subtle content changes under the print–scan (PS) distortions. To address these challenges, this work proposes a document image verification scheme based on paragraph alignment and subtle change detection. It first extracts paragraph structural features from both input and reference document images to achieve efficient image retrieval and accurate paragraph alignment. Based on the alignment results, the proposed scheme employs contrastive learning to reduce the effect of PS distortions in extracting features from the input and reference document images. Finally, an additional verification step is introduced that significantly reduces the false positive detection by addressing the feature misalignment within the extracted paragraphs. To evaluate the proposed scheme, extensive experiments were conducted on databases constructed from public datasets, and various benchmark methods were compared. Experimental results show that the proposed scheme outperforms benchmark methods, achieving an accuracy score of $0.963$.