Adversarial Training for Machine Learning Model Data Poisoning Resistance
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Solution Overview
Problem
Current solutions for defending against data poisoning attacks, particularly in machine learning models, are inadequate in addressing text-based attacks, which are less detectable and pose a significant threat to system security, as they often rely on visible patterns that can be easily exploited by attackers.
Innovation Solution
The introduction of adversarial training using augmented datasets that incorporate known types of data poisoning attacks, allowing the machine learning model to be resilient to such attacks and continually learn to detect new forms of data poisoning, combined with a backdoor auditing module for processing untrusted datasets to enhance system security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication and provenance-based solutions are used to detect data poisoning, then detection capability is improved, but system complexity increases due to cryptographic hash verification requirements
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing cryptographic hashes of clean training data before any poisoning attack can occur. These pre-computed hashes serve as reference points that enable rapid detection without requiring complex real-time verification procedures during actual attacks.
Solution Approach 2:
Cryptographic hashes act as an intermediary mechanism between the raw training data and the detection system. Instead of directly analyzing complex data patterns for poisoning, the system uses these hash values as simplified intermediaries that indicate whether data has been modified, reducing detection complexity while maintaining reliability.
2Measurement precision
If machine learning-based detection algorithms are used to identify anomalous examples, then detection accuracy is improved, but training time increases due to additional computational requirements
Solution Approach 1:
The system performs preliminary training on clean data to establish baseline model performance and store clean data representations. This preliminary action enables the model to quickly identify anomalies during actual poisoning attacks by comparing against pre-learned patterns, reducing the computational burden and time required for detection during training.
Solution Approach 2:
The system creates copies of clean training data and pre-computes their representations to serve as reference models. These copies are used during detection phases to compare against potentially poisoned data, allowing accurate anomaly identification without requiring the model to re-analyze all training data from scratch, thus reducing training time.
3Adaptability or versatility
If existing image-based detection methods are applied to text-based attacks, then solution availability is improved, but detection effectiveness deteriorates because text-based attacks are less detectable
Solution Approach 1:
The system adapts detection parameters and features specifically for text-based attacks by analyzing linguistic patterns, semantic relationships, and contextual anomalies in text data. This involves changing the detection focus from visual patterns (image-based) to linguistic and semantic parameters, enabling effective detection of text-based poisoning attacks while maintaining solution availability.
Solution Approach 2:
Instead of trying to detect visible attack patterns in text (which are often invisible or subtle), the system inverts the approach by detecting deviations from normal text patterns and semantic coherence. By establishing what normal text should look like and identifying anomalies, the system achieves effective detection of text-based attacks that would be invisible to traditional image-based methods.
4Reliability
If adversarial training with augmented datasets is implemented to create resilient models, then model security is improved, but data processing complexity increases due to need to process untrusted datasets
Solution Approach 1:
The system uses an intermediary auditing module that acts as a mediator between untrusted datasets and the main training process. This module performs preliminary analysis and validation of incoming data, filtering out obviously poisoned samples before they reach the main model training pipeline. This intermediary layer reduces the complexity of processing untrusted data by handling the most difficult cases separately.
Solution Approach 2:
The system performs preliminary actions by pre-training the model on clean, verified data and storing these representations as references. When untrusted data arrives, the system can quickly compare it against these pre-computed references to identify and filter out poisoned samples before they affect the main training process, reducing the overall data processing complexity while maintaining model security.
Data Source
AI summary
The techniques disclosed herein enable systems to train machine learning models using benign augmentation to enabled resistance various data poisoning attacks. This is achieved by first training a machine learning model using an initial dataset that is trustworthy and originates from a known source. The initial dataset is then modified to include known attack triggers such as syntactic paraphrasing to generate an augmented dataset. The augmented dataset is then used to train a robust machine learning model based using the initially trained machine learning model. The resultant robust machine learning model is then enabled to detect and resist attacks captured by the augmented dataset. The robust machine learning model can be retrained using an untrusted dataset that includes various compromised inputs in conjunction with the augmented dataset. Retraining results in an updated robust machine learning model that can learn and resist various data poisoning attacks on the fly.