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.
翻译:图像再捕捉攻击是一种有效的图像操纵方法,可以抹去某些法医痕迹,当瞄准个人文件图像时,它对电子商务和其他网络应用的安全构成极大的威胁,考虑到目前以学习为基础的方法存在严重的过度适应问题,在本文件中,我们提议建立一个新型的两分支深神经网络,通过开采更普遍的再捕文物,并配有设计频率过滤库和多尺度的交叉注意聚合模块。 在广泛的实验中,我们证明我们的方法能够比不同情景的先进技术实现更好的普及能力。