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The image recapture attack is an effective image manipulation method to erasecertain forensic traces, and when targeting on personal document images, itposes a great threat to the security of e-commerce and other web applications.Considering the current learning-based methods suffer from serious overfittingproblem, in this paper, we propose a novel two-branch deep neural network bymining better generalized recapture artifacts with a designed frequency filterbank and multi-scale cross-attention fusion module. In the extensiveexperiment, we show that our method can achieve better generalizationcapability compared with state-of-the-art techniques on different scenarios.

Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection