Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
This addresses security threats in e-commerce and web applications by improving detection of document image recapture attacks, though it appears incremental as it builds on existing learning-based methods.
The paper tackles the problem of document recapture attack detection by proposing a two-branch deep neural network with a frequency filter bank and multi-scale cross-attention fusion module, achieving better generalization capability compared to state-of-the-art techniques in experiments.
The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications. Considering the current learning-based methods suffer from serious overfitting problem, in this paper, we propose a novel two-branch deep neural network by mining better generalized recapture artifacts with a designed frequency filter bank and multi-scale cross-attention fusion module. In the extensive experiment, we show that our method can achieve better generalization capability compared with state-of-the-art techniques on different scenarios.