Model poisoning refers to an adversarial attack technique where malicious data is injected into a machine learning model’s training dataset. The aim is to compromise the model’s learning process, resulting in biased predictions or concealed weaknesses. This manipulation can diminish performance or permit targeted misclassifications during inference.
Context
In the realm of digital assets, model poisoning presents a substantial security risk to decentralized artificial intelligence applications and blockchain-integrated federated learning systems. Compromised models could yield erroneous market forecasts, flawed risk evaluations, or enable deceptive operations. Rigorous data validation and resilient training protocols are imperative to mitigate such attacks and safeguard model fidelity.
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