Definition ∞ Data Poisoning Defense refers to strategies and techniques employed to protect machine learning models from malicious manipulation of their training data. Attackers attempt to inject corrupted or misleading data into the training set, causing the model to learn incorrect patterns and make erroneous predictions. These defenses aim to detect and neutralize poisoned data points, ensuring the integrity and reliability of the model’s output. It is a critical security measure for AI systems operating in adversarial environments.
Context ∞ The discussion around data poisoning defense is gaining prominence as artificial intelligence becomes more integrated into critical systems, including those in finance and security. Its situation involves the increasing sophistication of adversarial attacks on AI models, necessitating advanced protective measures. A critical future development includes the application of blockchain technology for verifiable data provenance and immutable training logs to enhance defense capabilities. News often reports on new AI security research or vulnerabilities discovered in machine learning applications.
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