Length: 2 days
This 2-day course provides a comprehensive understanding of deepfake detection and attribution techniques, focusing on the latest advancements and their application in defense settings. Participants will learn about the challenges posed by deepfakes, explore various detection and attribution models, and gain hands-on experience with tools and techniques to counter deepfake threats.
Learning Objectives
- Understand the nature and threats of deepfakes to national security.
- Explore various deepfake detection algorithms and attribution techniques.
- Gain practical skills in using deepfake detection tools.
- Learn to implement solutions in offline environments.
- Understand ethical considerations and compliance with Responsible AI Guidelines.
Target Audience
- Defense personnel involved in cybersecurity, intelligence, and forensics.
- IT professionals and analysts working in defense-related fields.
- Cybersecurity researchers and practitioners.
Day 1: Understanding and Detecting Deepfakes
8:00 AM – 9:00 AM: Registration and Welcome
- Registration
- Course introduction and objectives
- Overview of the agenda
9:00 AM – 10:30 AM: Session 1 – Introduction to Deepfakes
- What are deepfakes?
- Techniques used to create deepfakes
- Case studies of deepfake usage in malicious activities
- Impact on national security and defense
10:30 AM – 10:45 AM: Break
10:45 AM – 12:15 PM: Session 2 – Deepfake Detection Techniques
- Overview of deepfake detection algorithms (CNNs, RNNs, GANs)
- Liveness detection: techniques and importance
- State-of-the-art detection methods
- Evaluating detection methods
12:15 PM – 1:15 PM: Lunch Break
1:15 PM – 2:45 PM: Session 3 – Practical Deepfake Detection
- Hands-on session with deepfake detection tools
- Real-time detection exercises
- Case study: Detecting deepfakes in operational scenarios
2:45 PM – 3:00 PM: Break
3:00 PM – 4:30 PM: Session 4 – Implementing Detection in Offline Environments
- Challenges of offline detection
- Self-contained detection modules
- Pre-loaded libraries and local data processing
- Practical exercise: Setting up an offline detection environment
4:30 PM – 5:00 PM: Q&A and Day 1 Wrap-up
- Recap of key points
- Q&A session
Day 2: Attribution, Ethical Considerations, and Implementation
8:00 AM – 9:00 AM: Recap and Day 2 Introduction
- Recap of Day 1
- Overview of Day 2 agenda
9:00 AM – 10:30 AM: Session 5 – Deepfake Attribution Techniques
- Attribution methods: identifying sources and methods
- Tools for attribution
- Case study: Tracing the origin of deepfakes
10:30 AM – 10:45 AM: Break
10:45 AM – 12:15 PM: Session 6 – Interface and Model Selection
- Developing a user-friendly interface for model selection
- Repository of detection models
- Testing and validating model results
- Practical exercise: Using the interface for model selection and testing
12:15 PM – 1:15 PM: Lunch Break
1:15 PM – 2:45 PM: Session 7 – Ethical Considerations and Responsible AI
- Overview of DIU’s Responsible AI Guidelines
- Ethical challenges in deepfake detection and attribution
- Developing an ethical framework
- Compliance and auditing processes
2:45 PM – 3:00 PM: Break
3:00 PM – 4:30 PM: Session 8 – Open Systems Architecture and Implementation
- Principles of open systems architecture
- Exporting evaluation and operational data in open formats
- Interoperability with existing DoD systems
- Practical exercise: Implementing open systems architecture
4:30 PM – 5:00 PM: Course Wrap-up and Certification
- Final Q&A session
- Course wrap-up and key takeaways
- Distribution of certificates of completion
Course Materials
- Slide deck covering all sessions
- Hands-on lab manuals for practical exercises
- Access to detection and attribution tools
- Responsible AI Guidelines documentation
Assessment and Certification
Participants will receive a certificate of completion upon successfully attending the full course and participating in practical exercises. An optional assessment can be provided to test the knowledge gained during the course.