Multi-Initialization Ensemble Defense Against Adversarial Attacks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning systems, particularly Convolutional Neural Networks (CNNs), are vulnerable to adversarial attacks in medical imaging, which can lead to misclassification of lung nodule malignancy and deceive both machine learning models and human recognition, posing a significant challenge in accurate diagnosis.
Innovation Solution
A multi-initialization ensemble-based defense strategy is employed, where multiple CNNs are trained using different initializations, and adversarial images are added to the training set to enhance robustness, with ensemble probability outputs improving malignancy prediction accuracy by combining pseudo probabilities from multiple CNNs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single CNN model is used for medical imaging classification, then the device complexity is low, but the reliability deteriorates due to vulnerability to adversarial attacks
Solution Approach 1:
The system segments the single CNN model into multiple independent CNN models, each trained with different initializations. These segmented models process adversarial examples independently, and their outputs are combined through ensemble voting to achieve higher reliability while maintaining individual model simplicity
Solution Approach 2:
Multiple CNN models are merged into an ensemble system where their classification results are combined. The ensemble approach merges the strengths of individual models to achieve robust classification against adversarial attacks, improving reliability through collective decision-making
2Reliability
If multiple CNNs with different initializations are used, then the reliability improves against adversarial attacks, but the device complexity increases
Solution Approach 1:
Instead of developing entirely different model architectures, the system creates copies of the same CNN structure with different initializations. These copied models are computationally efficient and easier to deploy while providing diverse perspectives for robust classification
Solution Approach 2:
The system changes the initialization parameters of multiple CNN models rather than modifying their architectural structure. This parameter-based differentiation allows creating diverse models with minimal complexity increase, as only the initial weight values differ while the overall model structure remains consistent
3Reliability
If adversarial images are added to the training set, then the reliability improves, but the loss of time increases due to additional training data processing
Solution Approach 1:
Adversarial examples are generated and added to the training set in advance before the main training process. This preliminary action allows the models to learn robust features from adversarial patterns during training, improving reliability without adding time during the actual classification phase
Solution Approach 2:
The system converts harmful adversarial examples into beneficial training data. By incorporating adversarial images into the training set, the models learn to recognize and resist attack patterns, transforming potential threats into opportunities for improving classification robustness
Data Source
AI summary
The present disclosure describes a multi-initialization ensemble-based defense strategy against an adversarial attack. In one embodiment, an exemplary method includes training a plurality of conventional neural networks (CNNs) with a training set of images, wherein the images include original images and images modified by an adversarial attack; after training of the plurality of conventional neural networks, providing an input image to the plurality of conventional neural networks, wherein the input image has been modified by an adversarial attack; receiving a probability output for the input image from each of the plurality of conventional neural networks; producing an ensemble probability output for the input image by combining the probability outputs from each of the plurality of conventional neural networks; and labeling the input image as belonging to one of the one or more categories based on the ensemble probability output.