Generalized Tampered Scene Text Detection in the era of Generative AI

• URL: http://arxiv.org/abs/2407.21422v1
• Date: Wed, 31 Jul 2024 08:17:23 GMT
• Title: Generalized Tampered Scene Text Detection in the era of Generative AI
• Authors: Chenfan Qu, Yiwu Zhong, Fengjun Guo, Lianwen Jin,
• Abstract summary: We present open-set tampered scene text detection, which evaluates forensics models on their ability to identify both seen and unseen forgery types. We introduce a novel and effective pre-training paradigm that subtly alters the texture of selected texts within an image and trains the model to identify these regions. We also present DAF, a framework that improves open-set generalization by distinguishing between the features of authentic and tampered text.
• Score: 33.38946428507517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancements of generative AI have fueled the potential of generative text image editing while simultaneously escalating the threat of misinformation spreading. However, existing forensics methods struggle to detect unseen forgery types that they have not been trained on, leaving the development of a model capable of generalized detection of tampered scene text as an unresolved issue. To tackle this, we propose a novel task: open-set tampered scene text detection, which evaluates forensics models on their ability to identify both seen and previously unseen forgery types. We have curated a comprehensive, high-quality dataset, featuring the texts tampered by eight text editing models, to thoroughly assess the open-set generalization capabilities. Further, we introduce a novel and effective pre-training paradigm that subtly alters the texture of selected texts within an image and trains the model to identify these regions. This approach not only mitigates the scarcity of high-quality training data but also enhances models' fine-grained perception and open-set generalization abilities. Additionally, we present DAF, a novel framework that improves open-set generalization by distinguishing between the features of authentic and tampered text, rather than focusing solely on the tampered text's features. Our extensive experiments validate the remarkable efficacy of our methods. For example, our zero-shot performance can even beat the previous state-of-the-art full-shot model by a large margin. Our dataset and code will be open-source.

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