Fine-Tuning Defense AI for Autonomous Systems and Surveillance

• An understanding of deep learning and computer vision architectures
• Experience with AI model training and evaluation using frameworks like TensorFlow or PyTorch
• Knowledge of defense-grade system requirements and security protocols

Audience

• Defense AI engineers
• Military tech developers
• Autonomous systems and surveillance platform architects

Fine-tuning is a critical process for adapting AI models to mission-specific defense applications, such as autonomous navigation and real-time surveillance.

This instructor-led, live training (online or onsite) is aimed at advanced-level defense AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems while meeting stringent security and reliability standards.

By the end of this training, participants will be able to:

• Fine-tune computer vision and sensor fusion models for surveillance and targeting tasks.
• Adapt autonomous AI systems to changing environments and mission profiles.
• Implement robust validation and fail-safe mechanisms in model pipelines.
• Ensure alignment with defense-specific compliance, safety, and security standards.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Overview of AI in Defense Applications

• Autonomous systems, UAVs, and real-time surveillance
• AI use cases in defense: navigation, tracking, reconnaissance
• Overview of AI model adaptation in mission-critical environments

Preparing Data for Fine-Tuning

• Working with sensor data: lidar, radar, thermal, and video feeds
• Labeling strategies for object detection and target recognition
• Data augmentation and anonymization in military contexts

Fine-Tuning AI Models for Perception and Control

• Vision models for real-time object detection and segmentation
• Fusion models for combining multi-sensor inputs
• Policy tuning for autonomous navigation and obstacle avoidance

Security, Safety, and Redundancy in AI Models

• Building resilient models with adversarial defense techniques
• Fail-safe design and anomaly detection during inference
• Securing model pipelines against tampering and spoofing

Testing and Simulation in Defense Environments

• Using synthetic data and digital twins for validation
• Stress testing under adversarial and extreme conditions
• Sim-to-real transfer in operational simulations

Compliance and Defense Standards

• AI assurance frameworks for defense deployments
• Security and ethics in autonomous defense applications
• Documenting compliance with operational and legal mandates

Deployment and Monitoring in the Field

• On-device inference and edge AI optimization
• Telemetry, feedback loops, and continual model updates
• Case studies from real-world defense AI systems

Summary and Next Steps