AI Model View-Level Analysis for Poisoned Data Recovery
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Solution Overview
Problem
AI models can be compromised by poisoned training data, leading to untrustworthy inferences and inefficient resource usage due to the need for re-training, which interrupts inference generation and consumes valuable computing resources.
Innovation Solution
Implement a system that periodically takes snapshots of AI models during training, allowing for efficient identification and remediation of poisoned data by analyzing the level of view a malicious party has into the model, thereby reducing computational resources required for re-training and mitigating the impact of poisoned training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are re-trained from scratch when poisoned data is detected, then the model reliability is restored, but computing resources and time are significantly consumed
Solution Approach 1:
The system performs preliminary actions by periodically saving snapshots of the AI model during training before poisoned data can completely compromise the model. When poisoning is detected, these pre-saved snapshots enable rapid restoration without requiring full re-training, thus restoring reliability while minimizing computing resource consumption.
Solution Approach 2:
The system discards the poisoned training data and recovers from previously saved model snapshots. Instead of wasting resources re-training from scratch, the system recovers the clean model state from snapshots taken before poisoning occurred, efficiently restoring reliability while avoiding redundant computation.
2Reliability
If full re-training is performed to remove poisoned data influence, then the model returns to untainted state, but inference generation is interrupted
Solution Approach 1:
Model snapshots are saved in advance during normal training operations, creating ready-to-use recovery points before poisoning occurs. When contamination is detected, the system can immediately restore from these pre-prepared snapshots, ensuring continuous inference generation without interruption while maintaining model purity.
Solution Approach 2:
The system creates copies of the model at various training stages through snapshots. When poisoned data is detected, instead of interrupting service for full re-training, the system copies the clean model state from a snapshot and restores it, maintaining inference generation continuity while ensuring model purity.
3Reliability
If periodic snapshots are taken during training, then remediation of poisoned data becomes efficient, but storage requirements increase
Solution Approach 1:
Instead of saving complete model states at every possible moment, the system takes periodic snapshots at strategically chosen intervals during training. This partial sampling approach provides sufficient remediation efficiency to restore from poisoning events while minimizing storage requirements by avoiding redundant or overly frequent snapshots.
4Reliability
If the system analyzes view level of malicious party to classify attack, then targeted remediation is achieved, but computational overhead increases
Solution Approach 1:
The system applies local quality analysis by examining specific aspects of the attack (view level of malicious party) rather than performing comprehensive analysis of all possible attack vectors. This targeted approach achieves accurate classification for remediation purposes while minimizing computational overhead by focusing only on relevant local characteristics of the poisoning attack.
Data Source
AI summary
Methods and systems for managing an attack on an artificial intelligence (AI) model using view level analysis are disclosed. As AI models are updated over time using new training data, snapshots of the AI models may be obtained. The snapshots may include information regarding the training data used to train the AI model, the parameters of the AI model, and/or the inferences obtained from the AI model. A malicious party may perform an attack on the AI model by introducing poisoned training data through a data source. The content of supplied poisoned training data may be determined based on a view level into the AI model. The view level of the malicious party may be used to design countermeasures to mitigate and/or prevent future attacks to the AI model.