Backdoor Detection in DNN Classifiers via Classification Margin Analysis
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Solution Overview
Problem
Existing methods for detecting and mitigating backdoor poisoning in deep neural networks (DNNs) are limited by their reliance on assumptions about the backdoor pattern and its method of incorporation, and they often require access to clean training data or supervised learning.
Innovation Solution
The proposed solution operates unsupervised and post-training, detecting and mitigating backdoor data-poisoning in DNN classifiers without assuming access to the training dataset. It uses a backdoor-agnostic approach that relies on the principle of overfitting to detect backdoors and limits internal activations of the DNN using a small clean dataset to mitigate backdoors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing backdoor detection methods are used, then detection capability is improved, but the methods require assumptions about backdoor patterns and access to clean training data
Solution Approach 1:
The system uses the DNN's own internal activations and classification margins to detect backdoors, without requiring external clean training data or supervised information. The method leverages the model's self-generated outputs (logits and activations) to identify anomalous patterns, making the detection process self-contained and autonomous.
Solution Approach 2:
The detection method is designed to be universally applicable to various backdoor patterns and incorporation methods without requiring specific assumptions about the attack type. The system can detect different backdoor configurations (trigger patterns, poisoning methods) using the same general framework of analyzing classification margins and activation statistics.
2Manufacturing precision
If clean training data is required for backdoor mitigation, then mitigation accuracy is improved, but data availability is reduced
Solution Approach 1:
The method extracts and analyzes specific features from the DNN's internal activations and classification margins to identify backdoor patterns. By focusing on the statistical properties of activations (such as maximum classification margins and activation distributions) rather than requiring complete clean datasets, the system can perform mitigation using minimal or no clean training data.
Solution Approach 2:
The system uses internal activations and classification margins as intermediary indicators to detect and mitigate backdoors. These intermediaries serve as proxies that reflect the presence of backdoor patterns without requiring direct access to clean training data. The intermediaries (activations and margins) are easily obtainable from the DNN's normal operation.
3Productivity
If the DNN is trained to maximize classification accuracy, then performance on clean data is improved, but susceptibility to backdoor poisoning increases
Solution Approach 1:
The system continuously monitors classification margins and activation patterns during DNN operation to provide feedback about potential backdoor conditions. By analyzing the feedback from activation statistics and classification margins, the system can identify when the model is overfitting to backdoor patterns and adjust accordingly to maintain robustness while preserving accuracy on clean data.
Data Source
AI summary
The disclosed embodiments disclose techniques for performing universal post-training backdoor detection and mitigation for classifiers. Mitigation of overfitting for a trained classifier begins with receiving the trained classifier and a clean dataset that spans a plurality of classes for the trained classifier. A set of input patterns are used to calculate classification margins for the trained classifier, and maximum classification margins are calculated for one or more classes of the trained classifier. Overfitting can then be mitigated by reducing one or more of these calculated maximum classification margins while maintaining the accuracy of the trained classifier for the clean dataset. In some embodiments, a backdoor detector may also detect target classes for a putative backdoor in the trained classifier upon detecting that the corresponding maximum classification margins for those target classes are anomalously high compared to the maximum classification margins of other classes.