  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.
