eyeo is the first in the industry to implement machine learning commercially to automate online ad detection.
Cologne - 28 July 2022 For the first time in history, our teams at eyeo have successfully deployed an effective machine learning solution to counter online circumvention of ad blockers, one of the biggest threats to achieving accurate ad-filtering. While some websites try to evade ad-blocking or ad-filtering technology, our work with machine learning models, while recognizably challenging, allows us to quickly respond to evasive measures and continue to deliver ad filtering that respects the user experience while providing value to publishers and advertisers.
Pioneering this technology gives our ad-filtering engine a competitive advantage over other ad blockers by being able to respond to these types of disruptions in a more robust way. Machine learning solutions are able to generalize i.e., detect ads that we've never seen before – those that classic ad filtering wouldn't detect. Also machine learning solutions are more difficult to circumvent.
Circumvention of ad blockers and then blocking that circumvention is a continual cat-and-mouse game, one without a roadmap to follow. However, as part of our goal to innovate, experiment and provide a better user and privacy-friendly online experience to as many users as possible, we set ourselves one huge goal – a moonshot – earlier last year.
Project Moonshot is our current foray into applying machine learning to our ad-filtering core to give our partners cutting-edge options to enhance their own products. By automating ad detection, we hope to filter the more intrusive ads while reducing human intervention of filter list creation and maintenance. We can also optimize ad blocking and ad filtering on mobile platforms and speed up our anti-circumvention efforts. All of this enriches our technology and creates a better product for our partners to distribute to their users.
To reduce time and resources spent, our team automated the pipeline to automate the collecting and preprocessing of the data needed to train the models. This gives developers more time to work on other important breakthroughs like using machine learning to quickly and effectively solve circumvention challenges as they appear.
“We took something tried and tested, such as the model architecture and machine learning methods, and deployed them in a brand new way in Project Moonshot. What’s interesting is that the way we collect the data and train for our specific use case has never been done commercially before. By going in this direction, we are able to offer ad-filtering technology in a way that nobody else can. I feel that these are the first small steps for automating ad filtering, but it’s one giant leap for the online world,” –– Humera Noor Minhas, Director of Engineering at eyeo.
To learn more about why we decided to experiment with machine learning for automating ad detection, check out part one, ‘Scaling ad filtering’, of our three-part blog series on Project Moonshot.
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Machine learning in ad filtering will be a topic at this year’s Ad-Filtering Dev Summit taking place in Amsterdam on 5 and 6 October 2022 and via live streaming: https://adfilteringdevsummit.com/#register
eyeo’s experts Humera Minhas and Parinitha Hirehal spoke at the 2021 conference and also at this year’s We Are Developers conference about the advances we are making in this area.
About eyeo
eyeo is dedicated to empowering a balanced and sustainable online value exchange for users, browsers, advertisers and publishers. By building, monetizing, and distributing ad-filtering technologies, we create solutions that allow all members of the online ecosystem to prosper. Our ad-filtering technology powers some of the largest ad blockers on the market, like Adblock Plus and Adblock, and is distributed through partnerships to millions of devices. We are constantly innovating to meet the expectations of the changing online world, with privacy solutions such as Crumbs, our white label browsers for distribution partners, and Acceptable Ads, which reaches over 225 million users.
For more information, please visit https://eyeo.com/press
