Learning to Locate the Text Forgery in Smartphone Screenshots

Zeqin Yu, Bin Li, Yuzhen Lin, Jinhua Zeng, Jishen Zeng

Abstract: In this paper, we present the Screenshot Text Forgery Dataset (STFD), which is the first public dataset for the smartphone screenshot text forgery localization task. To address such a task, we propose a novel Screenshot Text Forgery Localization Network (STFL-Net). Specifically, we introduce the OCR (Optical Character Recognition) stream as the complementary of the RGB stream, and propose a novel dual-stream Y-net architecture to collaboratively learn the representations focused on the traces on text regions of the image. Considering the text forgery is often subtle and local, we introduce a multi-teacher knowledge distillation learning strategy for training the STFL-Net, which makes the model less prone to over-fit one specific forgery trace. Comprehensive experimental results on STFD show that our method outperforms several previous methods designed for image forgery localization. We believe that, with our STFD dataset and STFL-Net, more advanced countermeasures against screenshot text forgeries can be developed in the future.