AI Model Snapshot Management for Poisoned Data Remediation
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Solution Overview
Problem
AI models can become untrustworthy when poisoned training data is introduced, leading to computationally expensive retraining processes that disrupt inference generation and consume valuable computing resources.
Innovation Solution
The system manages AI models by obtaining snapshots during the training process, which allow for the restoration of untainted models using a subset of the training dataset, reducing resource expenditure. Additionally, new training data is screened using directed modification analysis to identify potential poisoning, and remedial actions are taken based on the attacker's level of view into the pipeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are retrained from scratch when poisoned training data is detected, then the model reliability is restored, but computing resource consumption increases significantly
Solution Approach 1:
The system takes preliminary actions by obtaining snapshots of the AI model at different stages of the training process before poisoning occurs. These snapshots are stored in advance so that when poisoned training data is detected, the system can restore to a previous untainted snapshot without having to retrain from scratch, thereby reducing computing resource consumption while maintaining model reliability.
Solution Approach 2:
When poisoned training data is detected, the system discards the tainted training data and recovers the AI model by restoring from a previously saved snapshot. This approach eliminates the need to retrain the entire model from scratch, significantly reducing computing resource consumption while restoring model trustworthiness.
2Reliability
If full retraining is performed to remove poisoned data effects, then model reliability is improved, but training time increases
Solution Approach 1:
Snapshots of the AI model are obtained and stored in advance during the training process, before poisoning occurs. When poisoned training data is detected, the system can immediately restore to a previous snapshot, avoiding the time-consuming process of retraining from scratch and thus reducing the loss of time while maintaining model reliability.
Solution Approach 2:
The system discards the poisoned training data and recovers the model by restoring from a saved snapshot, which is much faster than performing full retraining. This approach improves model reliability while minimizing the time loss associated with remediation.
3Productivity
If snapshots are obtained during training to enable restoration, then computing resource efficiency is improved, but device complexity increases
Solution Approach 1:
The system creates copies of the AI model at different training stages in the form of snapshots. These snapshots are stored in a database or storage system, allowing restoration without retraining. This copying approach improves resource efficiency while the complexity is managed through automated snapshot management systems.
Solution Approach 2:
A snapshot management system acts as an intermediary between the training process and model restoration. This intermediary component handles snapshot creation, storage, and retrieval automatically, improving resource efficiency while containing complexity within a dedicated management layer rather than the core training logic.
4Reliability
If new training data is screened using directed modification analysis, then model reliability is improved, but processing time increases
Solution Approach 1:
New training data is screened using directed modification analysis before being integrated into the training dataset. This preliminary screening action identifies potentially poisoned data in advance, preventing it from corrupting the model. The time cost is incurred upfront during data ingestion, allowing faster model training and restoration operations later.
Data Source
AI summary
Methods and systems for managing an artificial intelligence (AI) model are disclosed. An AI model may be part of an evolving AI model pipeline, the processes of which may include obtaining training data from data sources used to update the AI model. An attacker may introduce poisoned training data via one or more of the data sources as a form of attack on the AI model. When the poisoned training data is identified, the one or more data sources that supplied the training data may be identified and analyzed to determine the attacker's level of view into the pipeline. Based on the attacker's level of view, remedial actions may be performed that may update operation of pipeline. The updated operation of the pipeline may reduce the computational expense for remediating impact of the poisoned training data, and may reduce the likelihood of obtaining poisoned training data in the future.