Forwarded from BlackBox (Security) Archiv
PimEyes - A Polish company just abolishes our anonymity
Research by netzpolitik.org shows the potential for abuse of PimEyes, a free search engine for 900 million faces. All of whom have photos on the Internet could already be part of their database.
Dylan smiles into the camera, arm in arm with the other guests of a queer boat party. Behind them, glasses glisten on the shelves of a bar. Eight years ago a party photographer uploaded this snapshot on the internet. Dylan had already forgotten it - until today. Because with a reverse search engine for faces, everyone can find this old party photo of Dylan. All they have to do is upload his profile picture from the Xing career network, free of charge and without registration. But Dylan wants to keep his private and professional life separate: During the day he works as a banker in Frankfurt am Main.
The name of the search engine is PimEyes. It analyses masses of faces on the Internet for individual characteristics and stores the biometric data. When Dylan tests the search engine with his profile picture, it compares it with the database and delivers similar faces as a result, shows a preview picture and the domain where the picture was found. Dylan was recognized even though, unlike today, he did not even have a beard then.
Our research shows: PimEyes is a wholesale attack on anonymity and possibly illegal. A snapshot may be enough to identify a stranger using PimEyes. The search engine does not directly provide the name of a person you are looking for. But if it finds matching faces, in many cases the displayed websites can be used to find out name, profession and much more.
👀 👉🏼 🇬🇧 PimEyes - A Polish company just abolishes our anonymity
https://netzpolitik.org/2020/pimeyes-face-search-company-is-abolishing-our-anonymity/
👀 👉🏼 🇩🇪: https://netzpolitik.org/2020/gesichter-suchmaschine-pimeyes-schafft-anonymitaet-ab/
👀 👉🏼 🇬🇧 https://www.bbc.com/news/technology-53007510
👀 👉🏼 🇬🇧 https://petapixel.com/2020/06/11/this-creepy-face-search-engine-scours-the-web-for-photos-of-anyone/
👀 👉🏼 🇩🇪 Automated face recognition -
Enforce our data protection rights at last!
https://netzpolitik.org/2020/automatisierte-gesichtserkennung-setzt-unsere-datenschutzrechte-endlich-auch-durch/
#PimEyes #facialrecognition #searchengine #privacy #anonymity #ourdata #thinkabout
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag
Research by netzpolitik.org shows the potential for abuse of PimEyes, a free search engine for 900 million faces. All of whom have photos on the Internet could already be part of their database.
Dylan smiles into the camera, arm in arm with the other guests of a queer boat party. Behind them, glasses glisten on the shelves of a bar. Eight years ago a party photographer uploaded this snapshot on the internet. Dylan had already forgotten it - until today. Because with a reverse search engine for faces, everyone can find this old party photo of Dylan. All they have to do is upload his profile picture from the Xing career network, free of charge and without registration. But Dylan wants to keep his private and professional life separate: During the day he works as a banker in Frankfurt am Main.
The name of the search engine is PimEyes. It analyses masses of faces on the Internet for individual characteristics and stores the biometric data. When Dylan tests the search engine with his profile picture, it compares it with the database and delivers similar faces as a result, shows a preview picture and the domain where the picture was found. Dylan was recognized even though, unlike today, he did not even have a beard then.
Our research shows: PimEyes is a wholesale attack on anonymity and possibly illegal. A snapshot may be enough to identify a stranger using PimEyes. The search engine does not directly provide the name of a person you are looking for. But if it finds matching faces, in many cases the displayed websites can be used to find out name, profession and much more.
👀 👉🏼 🇬🇧 PimEyes - A Polish company just abolishes our anonymity
https://netzpolitik.org/2020/pimeyes-face-search-company-is-abolishing-our-anonymity/
👀 👉🏼 🇩🇪: https://netzpolitik.org/2020/gesichter-suchmaschine-pimeyes-schafft-anonymitaet-ab/
👀 👉🏼 🇬🇧 https://www.bbc.com/news/technology-53007510
👀 👉🏼 🇬🇧 https://petapixel.com/2020/06/11/this-creepy-face-search-engine-scours-the-web-for-photos-of-anyone/
👀 👉🏼 🇩🇪 Automated face recognition -
Enforce our data protection rights at last!
https://netzpolitik.org/2020/automatisierte-gesichtserkennung-setzt-unsere-datenschutzrechte-endlich-auch-durch/
#PimEyes #facialrecognition #searchengine #privacy #anonymity #ourdata #thinkabout
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag
netzpolitik.org
A Polish company is abolishing our anonymity
An investigation by netzpolitik.org shows the potential for abuse of PimEyes, a free search engine for 900 million faces. Whoever’s photos have been published on the Internet could already be part of their database.
Forwarded from BlackBox (Security) Archiv
Image "Cloaking" for Personal Privacy
2020 is a watershed year for machine learning. It has seen the true arrival of commodized machine learning, where deep learning models and algorithms are readily available to Internet users. GPUs are cheaper and more readily available than ever, and new training methods like transfer learning have made it possible to train powerful deep learning models using smaller sets of data.
But accessible machine learning also has its downsides as well. A recent New York Times article by Kashmir Hill profiled clearview.ai, an unregulated facial recognition service that has now downloaded over 3 billion photos of people from the Internet and social media, using them to build facial recognition models for millions of citizens without their knowledge or permission. Clearview.ai demonstrates just how easy it is to build invasive tools for monitoring and tracking using deep learning.
So how do we protect ourselves against unauthorized third parties building facial recognition models to recognize us wherever we may go? Regulations can and will help restrict usage of machine learning by public companies, but will have negligible impact on private organizations, individuals, or even other nation states with similar goals.
The SAND Lab at University of Chicago has developed Fawkes1, an algorithm and software tool (running locally on your computer) that gives individuals the ability to limit how their own images can be used to track them. At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these "cloaked" photos as you normally would, sharing them on social media, sending them to friends, printing them or displaying them on digital devices, the same way you would any other photo. The difference, however, is that if and when someone tries to use these photos to build a facial recognition model, "cloaked" images will teach the model an highly distorted version of what makes you look like you. The cloak effect is not easily detectable, and will not cause errors in model training. However, when someone tries to identify you using an unaltered image of you (e.g. a photo taken in public), and tries to identify you, they will fail.
👀 👉🏼 http://sandlab.cs.uchicago.edu/fawkes/
#Fawkes #image #cloaking #facialrecognition #privacy
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag
2020 is a watershed year for machine learning. It has seen the true arrival of commodized machine learning, where deep learning models and algorithms are readily available to Internet users. GPUs are cheaper and more readily available than ever, and new training methods like transfer learning have made it possible to train powerful deep learning models using smaller sets of data.
But accessible machine learning also has its downsides as well. A recent New York Times article by Kashmir Hill profiled clearview.ai, an unregulated facial recognition service that has now downloaded over 3 billion photos of people from the Internet and social media, using them to build facial recognition models for millions of citizens without their knowledge or permission. Clearview.ai demonstrates just how easy it is to build invasive tools for monitoring and tracking using deep learning.
So how do we protect ourselves against unauthorized third parties building facial recognition models to recognize us wherever we may go? Regulations can and will help restrict usage of machine learning by public companies, but will have negligible impact on private organizations, individuals, or even other nation states with similar goals.
The SAND Lab at University of Chicago has developed Fawkes1, an algorithm and software tool (running locally on your computer) that gives individuals the ability to limit how their own images can be used to track them. At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these "cloaked" photos as you normally would, sharing them on social media, sending them to friends, printing them or displaying them on digital devices, the same way you would any other photo. The difference, however, is that if and when someone tries to use these photos to build a facial recognition model, "cloaked" images will teach the model an highly distorted version of what makes you look like you. The cloak effect is not easily detectable, and will not cause errors in model training. However, when someone tries to identify you using an unaltered image of you (e.g. a photo taken in public), and tries to identify you, they will fail.
👀 👉🏼 http://sandlab.cs.uchicago.edu/fawkes/
#Fawkes #image #cloaking #facialrecognition #privacy
📡@cRyPtHoN_INFOSEC_DE
📡@cRyPtHoN_INFOSEC_EN
📡@BlackBox_Archiv
📡@NoGoolag