论文标题
用于广义文档重新攻击检测的两个分支多尺度深神经网络
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
论文作者
论文摘要
图像重新攻击是一种有效的图像操纵方法，可以删除某些法医轨迹，并且针对个人文档图像时，它对电子商务和其他Web应用程序的安全构成了巨大威胁。考虑到当前基于学习的方法遇到了严重的过度拟合问题，在本文中，我们提出了一个新型的两分支深度神经网络，它通过挖掘更好的广义重新捕获工件，其设计频率过滤器库和多规模的跨意义融合模块。在广泛的实验中，我们表明，与不同情况下的最新技术相比，我们的方法可以实现更好的概括能力。
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.
