Modeling Deep Learning Based Privacy Attacks on Physical Mail
This work highlights a significant privacy vulnerability for individuals and organizations relying on physical mail, demonstrating that current paper envelopes are not secure against deep learning-based attacks.
This paper demonstrates a deep learning model, Neural-STE, that can recover hidden content from inside a sealed envelope using only an image of the envelope's front face. The model successfully recovers details like texture and image structure, showing that physical mail is vulnerable to such privacy attacks.
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.