Data Poisoning and Artificial Intelligence Modeling: Theoretical Foundations and Defensive Strategies

Data poisoning represents a significant and growing threat in the field of artificial intelligence (AI), compromising the reliability and integrity of machine learning (ML) models. This paper presents a comprehensive analysis of data poisoning attacks and their countermeasures, with three main contributions: (1) a systematic framework for understanding the theoretical foundations of data poisoning attacks, (2) a mathematical formulation of attack vectors and their impact on learning outcomes, and (3) a novel defensive approach based on the concept of "Dataset Core" that preserves information value while mitigating poisoning effects. By examining both attack mechanisms and defense strategies through a unified mathematical lens, we bridge the gap between theoretical understanding and practical defense implementation. Our proposed Dataset Core approach demonstrates promising potential for creating resilient ML systems that maintain performance integrity in adversarial environments, contributing to the secure deployment of AI in critical real-world applications