Enhancing AI robustness for more secure and reliable systems

By rethinking the way that most artificial intelligence (AI) systems protect against attacks, researchers at EPFL's School of Engineering have developed a training approach to ensure that machine learning models, particularly deep neural networks, consistently perform as intended, significantly enhancing their reliability. Effectively replacing a long-standing approach to training based on zero-sum game, the new model employs a continuously adaptive attack strategy to create a more intelligent training scenario. The results are applicable across a wide range of activities that depend on artificial intelligence for classification, such as safeguarding video streaming content, self-driving vehicles, and surveillance. The research was a close collaboration between EPFL's School of Engineering and the University of Pennsylvania (UPenn). In a digital world where the volume of data surpasses human capacity for full oversight, AI systems wield substantial power in making critical decisions.