论文标题
建模基于深度学习的隐私攻击对实物邮件
Modeling Deep Learning Based Privacy Attacks on Physical Mail
论文作者
论文摘要
邮件隐私保护旨在防止未经授权访问信封内隐藏内容，因为普通纸信封并不像我们想象的那么安全。在本文中，我们首次表明，使用精心设计的深度学习模型，隐藏的内容可以在很大程度上恢复而不打开信封。我们首先建模基于深度学习的隐私攻击对物理邮件内容的攻击，因为学习了从摄像头捕获的信封前面图像到隐藏内容的映射，然后我们将映射描绘成透视转换，图像去除，使用深度卷积神经网络（see-throughthrough-through-throff-envelope）的组合。我们通过实验表明，可以清楚地恢复隐藏的内容细节，例如纹理和图像结构。最后，我们的配方和模型使我们能够设计可以对抗实体邮件的深度学习隐私攻击的信封。
Mail privacy protection aims to prevent unauthorized access to hidden content within an envelope since normal paper envelopes are not as safe as we think. In this paper, for the first time, we show that with a well designed deep learning model, the hidden content may be largely recovered without opening the envelope. We start by modeling deep learning-based privacy attacks on physical mail content as learning the mapping from the camera-captured envelope front face image to the hidden content, then we explicitly model the mapping as a combination of perspective transformation, image dehazing and denoising using a deep convolutional neural network, named Neural-STE (See-Through-Envelope). We show experimentally that hidden content details, such as texture and image structure, can be clearly recovered. Finally, our formulation and model allow us to design envelopes that can counter deep learning-based privacy attacks on physical mail.
