Toward Real Text Manipulation Detection: New Dataset and New Solution
CoRR(2023)
摘要
With the surge in realistic text tampering, detecting fraudulent text in
images has gained prominence for maintaining information security. However, the
high costs associated with professional text manipulation and annotation limit
the availability of real-world datasets, with most relying on synthetic
tampering, which inadequately replicates real-world tampering attributes. To
address this issue, we present the Real Text Manipulation (RTM) dataset,
encompassing 14,250 text images, which include 5,986 manually and 5,258
automatically tampered images, created using a variety of techniques, alongside
3,006 unaltered text images for evaluating solution stability. Our evaluations
indicate that existing methods falter in text forgery detection on the RTM
dataset. We propose a robust baseline solution featuring a Consistency-aware
Aggregation Hub and a Gated Cross Neighborhood-attention Fusion module for
efficient multi-modal information fusion, supplemented by a Tampered-Authentic
Contrastive Learning module during training, enriching feature representation
distinction. This framework, extendable to other dual-stream architectures,
demonstrated notable localization performance improvements of 7.33% and 6.38%
on manual and overall manipulations, respectively. Our contributions aim to
propel advancements in real-world text tampering detection. Code and dataset
will be made available at https://github.com/DrLuo/RTM
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