🤖 AI Summary
Adversarial machine learning suffers from fundamental robustness deficiencies under evasion and poisoning attacks, undermining AI reliability in safety-critical applications.
Method: We propose the first unified mathematical framework formalizing diverse attack and defense classes, explicitly characterizing the inherent tension among certified robustness, scalability, and practical deployability. Our approach integrates game-theoretic modeling, optimization-theoretic analysis, formal verification, and empirical evaluation to establish a systematic, end-to-end analytical paradigm spanning the full attack–defense spectrum.
Contributions: (1) We identify theoretical and practical bottlenecks in robustness guarantees under adaptive adversaries; (2) We systematically characterize and structure three open challenges—ill-defined boundaries of certified robustness, insufficient scalability to large-scale settings, and lack of reliability in real-world deployment; (3) We provide verifiable theoretical benchmarks and principled design guidelines for next-generation robust AI systems.
📝 Abstract
Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance. This article provides a comprehensive analysis of evasion and poisoning attacks, formalizes defense mechanisms with mathematical rigor, and discusses the challenges of implementing robust solutions in adaptive threat models. Additionally, it highlights open challenges in certified robustness, scalability, and real-world deployment.