ΑΙhub.org
 

Machine learning technique to help keep your personal data your own

by
16 August 2021



share this:
AIhub | Facial recognition

By Sarah Monazam Erfani

You may not realise it, but without your consent the images you post on social media, including your profile, are harvested and used for training facial recognition systems driven by machine learning.

With this data, over which you now have almost no control, it’s easy for these systems to be used or misused to identify you and your friends perhaps, for example, from CCTV footage.

But what if there was a way to protect your data while still using it freely so that your friends can still see your photos but AI systems are blocked from exploiting these same images?

In our new research, we have shown that we can actually do this by using AI against itself to minimally adjust an image that makes it effectively ‘unlearnable’ to AI.

We have devised a machine learning-based technique that identifies and changes just enough pixels in an image to confuse AI, and turn it to an ‘unlearnable’ image. The change is very small and imperceptible to human eyes, but it introduces enough ‘noise’ into an image to make it useless for training AI.

With this technique, you could potentially simply tag your data with unlearnable noise that prevents it being exploited.

Many modern AI systems are based on artificial neural networks that learn to perform a task by repeatedly going through examples – like images of cats, if the AI is learning to identify cats. With each example, the program adjusts its parameters slightly to improve the results. This is what’s meant by ‘training’ AI. Researchers use the term “deep neural network” to differentiate sophisticated modern artificial neural networks.

This deep machine learning is now widely in use – from driving search engines to guiding medical surgery.

A key challenge for training deep neural networks is that the programs usually require a huge volume of data to learn from. The abundant ‘free’ data on the Internet has provided an easy solution to this.

Huge data sets are readily available, like the 80 Million Tiny Images collection and ImageNet, but they pose significant privacy and bias problems.

Our technique takes advantage of a key weakness in deep machine learning models – they are lazy learners. If the model believes that an example does not improve its performance – so, it’s an easy example that it has already learned – it will ignore it.

This weakness motivated the design of our model – our introduced noise fools deep neural networks into believing that there is nothing to learn from the protected images.

In addition, we set a constraint on the noise to ensure it is imperceptible to the human eye. Generally, a person wouldn’t notice a tiny change to an image that is less than 16 pixel values, however, this small change is sufficient to disrupt the model’s learning behaviour.

If someone uses unlearnable data to train deep learning models, the model will perform so poorly that it will be close to just making random guesses on new data.

The top row shows the original photos of the researchers, the bottom row have been modified to be ‘unlearnable’ to AI. Picture credit: Sarah Monazam Erfani

It should also be possible to use a similar technique to protect other types of data.

Take audio as an example, we could use sound pressure level (or acoustic pressure level) as the metric to measure the difference between the original audio and unlearnable audio.

A small change in sound pressure level is indistinguishable for humans, but this change can make the audio data unlearnable for machines.

It’s early days, but we believe this represents a step change in personal data protection, and there are several potential applications for the technology. For example, you could create unlearnable images before you share them on the Internet, protecting both yourself and your friends.

When a facial recognition model trained with your unlearnable online social media images and your unmodified pictures are captured by CCTV, the facial recognition system will not recognise you anymore.

On a larger scale, companies could release protected proprietary data without worrying it will be used for training deep learning models.

Our work only explores the technical challenges and opens up the possibilities. A lot of work still needs to be done for real-world applications, like developing an app that everyone can use.

But it is feasible.

People have a ‘digital right’ to control their own data. In the same way you can protect your house, you should be able to protect your data.

This article was first published on Pursuit. Read the original article.




Pursuit, University of Melbourne




            AIhub is supported by:


Related posts :



AIhub coffee corner: Is it the end of GenAI hype?

The AIhub coffee corner captures the musings of AI experts over a short conversation.
08 October 2024, by

ChatGPT is changing the way we write. Here’s how – and why it’s a problem

Have you noticed certain words and phrases popping up everywhere lately?
07 October 2024, by

Will humans accept robots that can lie? Scientists find it depends on the lie

Humans don’t just lie to deceive: sometimes we lie to avoid hurting others, breaking one social norm to uphold another.
04 October 2024, by

Explainable AI for detecting and monitoring infrastructure defects

A team of researchers has demonstrated the feasibility of an AI-driven method for crack detection, growth and monitoring.
03 October 2024, by

The Good Robot podcast: the EU AI Act part 2, with Amba Kak and Sarah Myers West from AI NOW

In the second instalment of their EU AI Act series, Eleanor and Kerry talk to Amba Kak and Sarah Myers West
02 October 2024, by

Forthcoming machine learning and AI seminars: October 2024 edition

A list of free-to-attend AI-related seminars that are scheduled to take place between 1 October and 30 November 2024.
01 October 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association