Exploring alternative threat models and certification approaches can lead to tighter robustness guarantees in machine learning. Some potential avenues include:
Adaptive Threat Models: Instead of static threat models, adaptive models that consider the capabilities of an adversary could be developed. These models would account for the potential strategies an attacker might employ, allowing for more robust certification against dynamic and sophisticated attacks.
Multi-Modal Threat Models: Incorporating multi-modal threat models that consider various types of attacks (e.g., data poisoning, adversarial examples, and model inversion) could provide a more comprehensive certification framework. This would enable FullCert to certify robustness against a wider range of potential vulnerabilities.
Probabilistic Certification Approaches: While FullCert focuses on deterministic guarantees, exploring probabilistic certification methods could yield tighter bounds in scenarios where deterministic guarantees are challenging to achieve. Techniques such as randomized smoothing or ensemble methods could be integrated to enhance robustness against specific types of attacks.
Hybrid Certification Techniques: Combining different certification techniques, such as interval-based methods with abstract interpretation or symbolic execution, could lead to more precise robustness guarantees. This hybrid approach would leverage the strengths of various methods to address the limitations of each.
Context-Aware Certification: Developing context-aware certification methods that consider the specific application domain and the nature of the data could lead to more tailored and effective robustness guarantees. This would involve analyzing the characteristics of the data and the model's behavior in different contexts to provide more relevant certifications.