P3F at Wearable Workshop of Financial Crypto 2015

Katharina presented an overview of different wearable Privacy Enhancing Technologies (PETs) at the Wearable Security & Privacy Workshop at the very prestigious Financial Cryptography 2015 conference. Monique, our very helpful manikin, posed for all the pictures and created a nice story arc for the presentation. You can get the paper from our download site.



Release of 1.0 Beta

We are proud, to release our first working Beta into public (visit “Downloads” in the page menu). There are still some limitations, the speed is not up to our wishes, and we like to include more patterns and encodings… but it is working! We added a “Visualization-Mode” so you can see what the software is actually doing in simple steps (see picture above).

First we detect all faces and than the bodies around it. From there we segment cloths and classify them into different pattern styles. Than we decode these patterns and apply the encoded Privacy Policy. Currently this only includes selective bluring of faces. Future versions will also remove GeoTags, etc..

This Proof-of-Concept Implementation requires Matlab; however the cheap Student-Edition including the Image-Toolbox is enough.


Academic Publications Overview

We are proud to give you this overview about the publications that resulted from our project so far:

Framework based on Privacy Policy Hiding for Preventing Unauthorized Face Image Processing
Adrian Dabrowski, Edgar R. Weippl, Isao Echizen
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC2013), DOI 10.1109/SMC.2013.83
[ PDF ] [ IEEE Xplore ] [ BibTex ]

Ok Glass, Leave me Alone: Towards a Systematization of Privacy Enhancing Technologies for Wearable Computing
Katharina Krombholz, Adrian Dabrowski, Matt Smith, Edgar R. Weippl
Proceedings of Workshop on Wearable Security and Privacy co-located with Financial Cryptography and Data Security 2015
[ PDF ]

Tag Detection for Preventing Unauthorized Face Image Processing
Alberto Escalada Jimenez, Adrian Dabrowski, Juan M Montero Martinez, Isao Echizen
The 13th International Workshop on Digital-forensics and Watermarking (IWDW 2014)
[ pre-print PDF ] [ BibTex ]

Manikin Monika is helping out


New staff for our project! ‘Monika’ is helping out with test shots for our project. Katharina needs to focus more on programing again (seems to make her happy). This week she will visit SOUPS (Symposium on usable privacy and security) at Facebook headquarters. Let’s see, how our project is received there and what the newest developments in the sector are.

Testing Person Correlation

Our first tests with person correlation where performed with QR-Codes. In this example, we used QR codes to encode privacy policies. The overall goal of this experimental setup was to test person correlation inherently to the artifacts in which the privacy policies are encoded. We are not intending to use QR codes in a later stage of the project as they are big, intrusive and everything else but aesthetic. However, due to their feasibility for this particular test on person correlation they serve as an excellent baseline for investigation.

One of the essentials of the P3F framework is efficient and reliable face detection in order to correlate the persons on the picture with the artifacts used to encode the privacy policies.

Our results have shown, that false positives from the face detector can lead to very unpleasant effects for the users. In case of a fale positive, the P3F tags are assigned away from real persons to falsely detected faces that coincidentely happen to be nearer. Thus, they weaken the personal protection of the actual persons in the picture. We will address this problem by revising our framework and by testing several face detection algorithms that have been investigated in scientific publications and used in industrial applications.


Criteria for Marking Patterns

Technical requirements:

a) Illumination stability:
The code should be decodable under a wide range of lighting conditions. However, under conditions making face identification impossible, a decoding failure is tolerable.
b) Blurriness tolerance:
Picture blurriness can arise from sub-optimal auto-focus mechanisms because the photographer actually focused on another object or person or moved the camera during exposure (a common problem with amateur photographers).
c) Size and clipping invariance:
The code should be decodable from shots with different fields of view. Therefore, it should be so redundant that a partial capture in a close-up produces results as good as those in a wide shot. Furthermore, in a wide shot, a larger part of the code is recorded but with a reduced resolution compared to a close-up. Fine encoding that repeats multiple times is better for close-up shots while coarse encoding is better for wide shots. Ideally, a code unifies both traits.
d) Distortion stability:
People do not always face the camera head-on, especially when they are being photographed unintentionally. Furthermore, the human body is not a flat board, and loose clothing tends to fold and wrinkle. Another faults may arise from lense distortion or improper washing or drying of the person’s clothing.
e) Noise robustness:
Another artifact introduced by cameras is noise, especially in low-light and low-contrast situations due to the automatic camera gain amplifying the sensors background noise.
f) Computational weight:
The detection algorithm should be lightweight because operators of publishing systems will most likely demand one that conserves computational resources.
g) Compression stability:
Digital photography greatly depends on picture compression algorithms. They commonly destroy details in pictures and introduce artifacts. These algorithms are often based on a psycho-visual model of human visual perception and are therefore not optimized for computer vision purposes. The most common compression method for photographs on the Internet is JPEG.
h) Blind decoding ability:
The decoder should have the ability to decode the data without prior knowledge of the original pattern used to encode the data or the data that is being looked for (a common prerequisite for some watermarking techniques).
i) Detection accuracy:
Detection accuracy should be high with a slight bias toward false positives since people typically feel more comfortable with more privacy than with less. False positives can still be overridden by the publisher if necessary.
j) Error detection and correction:
The encoding scheme should have an error detection or correction code to avoid producing erroneous results.

Aesthetic Demands:

a) Dress code:
Dress codes are often imposed by society, the employer, or another external entity. The coding scheme should thus produce markings and patters that blends into the imposed dress code.
b) Fashion:
People additionally often have their own fashion demands. The coding scheme should thus produce markings that blends into the individual’s fashion style.
c) Adaptive:
Clothing is sold in many different colors and shapes. The code should thus be versatile and work with many different colors and shapes.
d) Unobtrusive:
The application of P3F should require only a slight adjustment in clothing style. The code should be subtle with low visual impact. It should be unrecognizable by other people, thus minimizing social complications.