Detecting Intentional Dataset Poisoning Through Training-Time Gradient Behavior and Internal Data Contradictions

Machine learning models are highly dependent on the integrity of training data, making them vulnerable to dataset poisoning and backdoor attacks. Such manipulations can subtly alter model behavior while maintaining normal validation performance, making detection challenging. Existing approaches largely focus on post-training inspection or trigger reconstruction, which may fail to provide early-stage protection. This paper proposes a structured training-time framework for detecting intentional dataset poisoning by monitoring behavioral learning signals during model optimization. The methodology integrates gradient anomaly analysis, internal contradiction detection, signal aggregation, and intent-based inference to evaluate dataset integrity. By distinguishing structured adversarial patterns from natural training instability, the framework provides a probabilistic risk assessment prior to deployment. A conceptual experimental design demonstrates how persistent gradient bias and class-level instability can indicate potential malicious manipulation. The proposed approach introduces an intent-aware detection mechanism that enhances early-stage adversarial resilience in machine learning systems.