Modeling Deep Learning Based Privacy Attacks on Physical Mail

TL;DR

This paper demonstrates that deep learning models can largely recover hidden content inside envelopes from images, revealing privacy vulnerabilities and enabling the design of more secure envelopes.

Contribution

It introduces Neural-STE, a deep learning model that maps envelope images to hidden content, highlighting privacy risks and proposing countermeasures.

Findings

Deep learning can recover hidden envelope content effectively.

Neural-STE models perspective, dehazing, and denoising for content recovery.

Proposes envelope designs to counter privacy attacks.

Abstract

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.