基于深度学习的实体邮件隐私攻击建模
Modeling Deep Learning Based Privacy Attacks on Physical Mail
AI总结：
本文首次证明深度学习模型可在不打开信封的情况下恢复隐藏内容，通过将攻击建模为透视变换、去雾和去噪的组合，并提出Neural-STE网络实现，同时为设计防御性信封提供了基础。
摘要：
邮件隐私保护旨在防止未经授权访问信封内的隐藏内容，因为普通纸质信封并不像我们想象的那样安全。在本文中，我们首次证明，借助精心设计的深度学习模型，无需打开信封即可在很大程度上恢复隐藏内容。我们首先将针对实体邮件内容的基于深度学习的隐私攻击建模为学习从相机拍摄的信封正面图像到隐藏内容的映射，然后我们使用名为Neural-STE（透视信封）的深度卷积神经网络，将该映射显式建模为透视变换、图像去雾和去噪的组合。我们通过实验表明，隐藏内容的细节（如纹理和图像结构）可以被清晰地恢复。最后，我们的公式和模型使我们能够设计出能够抵御针对实体邮件的基于深度学习的隐私攻击的信封。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
