AI Model Snapshot Restoration for Poisoned Inference Management
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Solution Overview
Problem
AI models can become untrustworthy due to poisoned training data, leading to inaccurate inferences that impact inference consumers, and re-training these models is computationally expensive and resource-intensive.
Innovation Solution
The system manages AI models by periodically taking snapshots during training, allowing for the restoration of a partially trained untainted model, and evaluates the impact of poisoned inferences on inference consumers to determine if remediation is necessary, thereby reducing resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If re-training AI models is performed to remove poisoned training data, then model reliability is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by periodically capturing snapshots of the AI model during training before poisoned data can corrupt the model. These snapshots serve as backup copies that can be restored quickly when poisoning is detected, eliminating the need for complete re-training and significantly reducing computational resource consumption while maintaining model reliability.
Solution Approach 2:
Instead of discarding the entire model and re-training from scratch, the system recovers by restoring to a previous snapshot state. This selective recovery approach retains all the computational work done up to the snapshot point and only needs to re-train from that point forward, dramatically reducing the computational resources required compared to full re-training.
2Measurement precision
If complete re-training is performed to eliminate poisoned inferences, then inference accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing model snapshots at regular intervals during training. When poisoning is detected, these pre-captured snapshots enable rapid restoration to a known good state, significantly reducing the time required to eliminate poisoned inferences compared to waiting for complete re-training to finish.
Solution Approach 2:
The system discards the poisoned model state and recovers by restoring to a previous snapshot. This recovery mechanism eliminates the need to wait for complete re-training cycles, thereby reducing time consumption while maintaining inference accuracy through restoration to a verified good state.
3Reliability
If poisoned training data is removed from the dataset, then model trustworthiness is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing snapshots of the model state during training. When poisoned data is identified, these snapshots provide a quick restoration point that isolates the poisoned data's impact without requiring complex data processing or cleaning operations, thereby improving trustworthiness while minimizing data processing complexity.
Solution Approach 2:
Instead of engaging in complex data processing to remove or clean poisoned data, the system discards the poisoned model state and recovers by restoring to a previous snapshot. This approach simplifies the overall process by avoiding complex data cleaning operations while maintaining model trustworthiness through restoration to a verified good state.
Data Source
AI summary
Methods and systems for managing impact of inferences provided to inference consumers on the operation of the inference consumers are disclosed. Poisoned training data may be introduced and used to train an AI model, which may then poison the AI model and lead to poisoned inferences being provided to the inference consumers. To determine whether to remediate the poisoned inferences, a replacement inference may be generated and consumed by a digital twin of the inference consumers. A quantification of deviation of operation between the inference consumers after consuming the poisoned inference and operation of the digital twin after consuming the replacement inference may be compared to a threshold. If the quantification meets the threshold, an action set may be performed to remediate the impact of the poisoned inference.