02/02/2021

Modeling Deep Learning Based Privacy Attacks on Physical Mail

Bingyao Huang, Ruyi Lian, Dimitris Samaras, Haibin Ling

Keywords:

Abstract Paper Similar Papers

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.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38948224
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 16:04 
 0
 0
 0
 0
 13:29 
 0
 0
 0
 0
 20:02 
 0
 0
 0
 0
 15:57 
 0
 0
 0
 0
 15:37 
 0
 0
 0
 0
 13:35 
 0
 0
 0
 0
 14:59 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 12:40 
 0
 0
 0
 0
 18:14 
 0
 0
 0
 0
 3:14 
 0
 0
 0
 1
 15:50 
 0
 0
 0
 0
 12:17 
 0
 0
 0
 0
 14:14 
 0
 0
 0
 0
 5:04 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 17:14 
 0
 0
 0
 0
 6:06 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 16:19 
 0
 0
 0
 0
 4:48 
 0
 0
 0
 2
 4:57 
 0
 0
 0
 0
 14:17 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 20:43 
 0
 0
 0
 0
 4:25 
 0
 0
 0
 0
 11:05 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 15:05 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 19:26 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 13:35 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 11:35 
 0
 0
 0
 0
 5:20 
 0
 0
 0
 0
 15:44 
 0
 0
 0
 0
 2:59 
 0
 0
 0
 0
 9:36