These systems focus on defending AI models against adversarial attacks, such as evasion, poisoning, and model extraction. They employ techniques like adversarial training, defensive distillation, and adversarial example detection to enhance model robustness and resilience.

Homomorphic encryption enables computations on encrypted data without the need for decryption. This technology can be applied to AI systems to ensure data privacy and security during model training and inference. Secure Multi-party Computation (MPC) for AI Systems: MPC allows multiple parties to jointly compute a function without revealing their individual inputs. This approach can be used to train AI models on sensitive data while preserving data privacy and confidentiality.

Differential privacy is a system designed to protect individual privacy in large datasets by adding calibrated noise to the data. This technique can be applied to AI systems to ensure privacy during data collection and model training.

These systems embed unique fingerprints or watermarks into AI models, making it possible to trace unauthorized model usage or identify the source of model leaks.

Hardware security modules provide a secure environment for storing and managing cryptographic keys, which can be used to protect AI models and data during storage and transmission.

Federated learning enables collaborative model training across multiple devices without sharing raw data. Secure federated learning systems enhance privacy and security by employing techniques such as secure aggregation, differential privacy, and homomorphic encryption. These AI-specific security systems and technologies contribute to safeguarding AI models, data, and intellectual property in various domains and industries.