A Comprehensive Survey of AI Security Threats in IoT and Edge Cloud Systems
This survey research work proposes a lifecycle-based understanding of AI security threats and proposed a unified taxonomy for the four major categories of threats observed in real-world settings, namely, data poisoning and backdoor attacks on learning model updates, adversarial attacks on model outputs through input manipulation, privacy leakage through model-based queries, and model extraction for intellectual property theft and creation of rogue replicas of learning models.