This webcast illustrated where machine learning applications can be attacked, the means for carrying out the attack and some mitigations that can be employed. The elements in building and deploying a machine learning application are reviewed, considering both data and processes. The impact of attacks on each element is considered in turn. Special attention is given to transfer learning, a popular way to construct quickly a machine learning application. Mitigations to these attacks are discussed with the engineering tradeoffs between security and accuracy. Finally, the methods by which an attacker could get access to the machine learning system were reviewed. Speaker: Dr. Mark Sherman

Ver todos los episodios