AI Model Snapshot Analysis for Poisoned Training Data Detection
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Solution Overview
Problem
Trained AI models can become untrustworthy due to poisoned training data, leading to untrustworthy inferences and inefficient re-training processes that consume significant computing resources, disrupting inference consumption and other services.
Innovation Solution
Periodically obtain snapshots of AI models and analyze changes to identify poisoned training data using transfer learning, allowing for computationally efficient remediation by reverting to a last known good model and replacing poisoned inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If re-training is performed to remove poisoned training data influence, then model reliability is improved, but computing resource consumption increases and service disruption occurs
Solution Approach 1:
The system performs preliminary detection of poisoned training data by analyzing model characteristics before complete re-training is necessary. By detecting poisoning early through characteristic analysis and comparing model states, the system can take preliminary remediation actions that reduce the extent of re-training required, thereby saving computing resources while still improving model reliability.
Solution Approach 2:
The system identifies and discards only the poisoned portions of training data rather than discarding all training data. By recovering and retaining the valid training data, the system minimizes the re-training workload while still removing the harmful influence of poisoned data, thus improving reliability with reduced computing resource consumption.
2Reliability
If re-training is performed to remove poisoned training data influence, then model reliability is improved, but service continuity deteriorates
Solution Approach 1:
The system performs preliminary detection and identification of poisoned training data characteristics before complete re-training is initiated. This preliminary action allows for faster remediation by targeting only the affected portions of the model, reducing the time required for re-training and minimizing service downtime while still improving model reliability.
Solution Approach 2:
The system discards only the poisoned training data and recovers the valid training data for continued use. This selective approach allows the model to be updated with minimal re-training, maintaining service continuity while improving reliability by removing only the harmful data influence.
3Reliability
If complete re-training is performed to ensure model reliability, then model trustworthiness is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary detection of poisoned training data by analyzing model characteristics and comparing model states before complete re-training. This preliminary detection enables targeted remediation that maintains model trustworthiness while avoiding the productivity loss associated with complete re-training of the entire model.
Solution Approach 2:
The system discards only the poisoned training data and recovers the valid training data, allowing the model to be updated with minimal re-training. This approach maintains model trustworthiness by removing harmful data while preserving productivity by retaining and reusing the valid training data portion.
Data Source
AI summary
Methods and systems for managing artificial intelligence (AI) models are disclosed. To manage AI models, AI models may be updated over time to obtain updated AI model instances. Following each update process, the updated instance of the AI model may be analyzed to determine whether poisoned training data was used to update the AI model. To perform the analysis, characteristics associated with the updated instance of the AI model may be compared to characteristics of the previous instance of the AI model. If the characteristics of the updated instance of the AI model differ from the characteristics of the previous instance of the AI model by an amount dictated by a threshold, the training data used to obtain the updated instance of the AI model may be treated as including poisoned training data.