Nura Kawa
Machine Learning: Promising, but Perilous
#1about 5 minutes
The dual nature of machine learning's power
Machine learning's increasing power and accessibility, exemplified by complex tasks like panoptic segmentation, also introduces significant security vulnerabilities.
#2about 3 minutes
Accelerating development with transfer learning
Transfer learning allows developers to repurpose large pre-trained teacher models for specific tasks with minimal data and compute by fine-tuning a new student model.
#3about 2 minutes
How transfer learning's benefits create security risks
The core benefits of transfer learning, such as knowledge transfer and minimal training, directly create attack vectors for adversaries.
#4about 5 minutes
Exploring evasion and poisoning attacks in ML
Adversarial examples can fool models with subtle input changes (evasion), while poisoned data can insert hidden backdoors, with both risks amplified by transfer learning.
#5about 3 minutes
Integrating security with a pre-development risk assessment
Before writing code, perform a thorough risk assessment by defining security requirements, evaluating resource availability, and conducting threat modeling for your specific use case.
#6about 3 minutes
Selecting robust teacher models for secure transfer learning
Mitigate risks by choosing transparent and trustworthy teacher models and using robust models hardened through techniques like adversarial training.
#7about 1 minute
Fortifying student models to prevent transferred attacks
Strengthen your student model by fine-tuning all layers to diverge from the teacher model, using backdoor detection, and performing continuous stress testing.
#8about 2 minutes
Key resources for developing secure ML systems
Practical resources like the Adversarial Robustness Toolbox for developers and security principles from the National Cybersecurity Center can help you build more secure ML systems.
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