Detecting Tampered Scene Text in the Wild

Abstract

Text manipulation technologies cause serious worries in recent years, however, corresponding tampering detection methods have not been well explored. In this paper, we introduce a new task, named Tampered Scene Text Detection (TSTD), to localize text instances and recognize the texture authenticity in an end-to-end manner. Different from the general scene text detection (STD) task, TSTD further introduces the fine-grained classification, i.e. the tampered and real-world texts share a semantic space (text position and geometric structure) but have different local textures. To this end, we propose a simple yet effective modification strategy to migrate existing STD methods to TSTD task, keeping the semantic invariance while explicitly guiding the class-specific texture feature learning. Furthermore, we discuss the potential of frequency information for distinguishing feature learning, and propose a parallel-branch feature extractor to enhance the feature representation capability. To evaluate the effectiveness of our method, a new TSTD dataset (Tampered-IC13) is proposed and released at https://github.com/wangyuxin87/Tampered-IC13.