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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.
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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.”
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:
- the first neural net works to identify faces — called the detection AI
- the second neural net works to disrupt the facial recognition task of the first — called the disruptive AI
- 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.
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.”
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.”
Univ. of Toronto | home
Parham Aarabi PhD | home
Avishek Joey Bose | home
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.
read | story
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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
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