Neural Network Verification with PyRAT

There is no doubt that Artificial Intelligence (AI) has taken over an important part of our lives and its recent popularisation with large language models has anchored this change even more in our current landscape. The use of AI is becoming more and more widespread reaching new sectors such as health, aeronautics, energy, etc., where it can bring tremendous benefits but could also cause environmental, economic, or human damage, in critical or high-risk systems. In fact, numerous issues are still being uncovered around the use of AI, ranging from its lack of robustness in the face of adversarial attacks [1, 2], to the confidentiality and privacy of the data used, the fairness of the decisions, etc. Faced with these threats and the exponential growth of its use, regulations have started to emerge with the European AI Act. Not waiting for regulations, tools have already been developed to respond to and mitigate these threats by providing various guarantees from the data collection phase to AI training and validation of the AI. Our interest here lies in this last phase, the formal validation of an AI system, and more specifically a neural network, to allow its use in a high-risk system.