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AI software tool disables automated facial tracking « Kurzweil

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Engineering researchers at the University of Toronto, in Canada — used AI software programs to design a privacy filter for your photos that disables automatic facial recognition systems.

Each time you upload a photo or video to a social media platform, its facial recognition systems learn a little more about you. These algorithms ingest data about who you are, your location, and people you know — and they’re constantly improving.

As concerns over privacy + data security on social networks grow, Univ. of Toronto engineering researchers — led by Parham Aarabi PhD and graduate student Avishek Bose — have created a computer software algorithm to dynamically disrupt facial recognition systems.

What is facial recognition?

A facial recognition system is a technology capable of matching a human face — found in a digital photo, graphic image, or in a video frame — against a data-base of faces. Researchers are developing many types of facial recognition systems. The most advanced method — used to authenticate people through ID verification services — can pinpoint + measure distinct facial features in an image. The process of measuring human physical characteristics is called bio-metrics.

Face recognition is commonly used on smart-phones and in robotics. Its accuracy as a bio-metric tech is lower than iris recognition, and fingerprint recognition. But it’s widely used because it’s contact-less and non-invasive, especially for video surveillance and automatically indexing images.


— reference —

blog: Joey Bose
paper title: Adversarial attacks on face detectors using neural net based constrained optimization
read | paper


Facial recognition gets better + better.

Parham Aarabi PhD said: “Personal privacy is a real issue as facial recognition becomes better + better. This is one way beneficial anti-facial-recognition systems can combat that ability.”

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Their solution leverages a deep learning technique called adversarial training, which pits 2 AI algorithms against each other. In computing, deep learning and is a math technique that uses complex sets of data — trained to find solutions to problems — to process information. Inspired by the biology of human thinking, deep learning helps computers quickly recognize and process images + speech. Like all techniques in the computer software field of AI — deep learning is good at recognizing hard-to-find patterns in big data sets.

Computer systems called neural networks run AI software that can achieve astounding human-level abilities of pattern recognition. With their deep learning algorithms, they can process in seconds what takes human analysts weeks, months, or years.

A neural network uses a series of deep learning algorithms to recognize underlying relationships in a set of data through a process that mimics human reasoning. The researchers harnessed the power neural networks to engineer a system that could block automated facial recognition.

How it works.

Aarabi and Bose designed a set of 2 neural networks:

  1. the first neural net works to identify faces — called the detection AI
  2. the second neural net works to disrupt the facial recognition task of the first — called the disruptive AI
  3. the 2 neural nets constantly battle + learn from each other

The result is an automated software filter that can be applied to photos, to protect a user’s privacy. Their algorithm alters very specific pixels in the image, making changes that are almost imperceptible to the human eye.

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Bose said: “The disruptive AI can attack what the neural net for the face detection is looking for. For example, if the detection AI is looking for the corner of the eyes — it adjusts the corner of the eyes so they’re less noticeable. It creates very subtle disturbances in the photo, but to the detector they’re significant enough to fool the system.”

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Aarabi and Bose tested their software on an industry standard pool of more than 600 faces — called the 300-W face data-set. This data-set includes a wide range of ethnicity, lighting conditions, and environments. Their system successfully reduced the number of faces that were originally detectable from 100 % — down to 0.5 %

Bose said: “The key is training 2 neural networks against each other — one creates an increasingly robust facial detection system, and the other creates an even stronger tool to disable facial detection.



A powerful tool.

In addition to disabling facial recognition, the new tech also:

  • disrupts image-based search
  • disrupts feature identification
  • disrupts emotion + ethnicity estimation
  • disrupts all other face-based attributes that could be extracted automatically

Next, the team hopes to make the privacy filter publicly available — as a smart-phone app, mobile tablet app, or website.

Aarabi said: “10 years ago these software algorithms would have to be human-defined. But now neural nets learn by themselves — you don’t need to supply them anything except training data. They can do some really amazing things. It’s a fascinating time in the computer field, there’s enormous potential.”


reference


Univ. of Toronto | home

Parham Aarabi PhD | home
Avishek Joey Bose | home

 


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group: Univ. of Toronto
motto: As a tree through the ages.

publication: University of Toronto Magazine
tag line: Celebrating the university’s research + teaching excellence.

story title: Engineering AI researchers design privacy filter for your photos that disables facial recognition systems
read | story

— summary —

Each time you upload a photo or video to a social media platform, its facial recognition systems learn a little more about you. These algorithms ingest data about who you are, your location, and people you know — and they’re constantly improving. As concerns about privacy + data security on social networks grow, engineering researchers have created an algorithm to dynamically disrupt facial recognition systems.

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group:
tag line:

publication:
tag line:
story title:
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— notes —

* AI = artificial intelligence
* NN = neural network
* DL = deep learning


[ story file ]

story title: digest | AI software tool disables automated facial tracking
deck: As privacy + security concerns increase, artificial intelligence finds solutions.
folder: stories on progress

[ end of file ]



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