As AI adoption accelerates, protecting image data from unauthorized model training has become a growing concern. Traditional “unlearnable” data techniques are increasingly ineffective against modern ensemble learning attacks, leaving sensitive or proprietary content exposed to misuse.
Our technology advances ensemble learning—specifically boosting, bagging, and stacking—by incorporating targeted data augmentation methods that consistently overcome twelve leading unlearnable data defenses. It restores model accuracy to over 89% on protected CIFAR-10 datasets, integrates into existing training workflows, scales efficiently across computing platforms, and supports compliance with data privacy regulations such as GDPR. This solution empowers organizations to evaluate and reinforce their AI privacy strategies without compromising performance.
Proposed ensemble learning frameworks—Boosting, Stacking, and Bagging—designed to defeat state-of-the-art unlearnable data defenses.