Adversarial Training via Activation Path Differentials
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Solution Overview
Problem
Deep neural networks are vulnerable to adversarial attacks, which can cause misclassification and learning errors due to subliminal signals in input data, and existing defense techniques require a large number of adversarial examples and suffer from accuracy loss and convergence issues.
Innovation Solution
The method involves adversarial training using activation path differentials to identify and protect the most vulnerable paths within neural networks, reducing the need for extensive adversarial examples and improving robustness and computational efficiency by focusing on essential visual changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional adversarial training is used to defend against adversarial attacks, then the neural network's robustness is improved, but the computational cost and training time increase significantly due to requiring large numbers of adversarial examples
Solution Approach 1:
The patent extracts and focuses training efforts only on the most vulnerable activation paths within the neural network, rather than uniformly training across all paths. By identifying and isolating critical vulnerable paths through activation path differential analysis, the method reduces the amount of adversarial examples needed while maintaining robustness improvement.
Solution Approach 2:
The patent applies local quality by differentiating training intensity across different activation paths. Instead of uniform adversarial training, the method identifies specific vulnerable paths and applies targeted training adjustments to those regions, allocating computational resources proportionally to vulnerability levels rather than uniformly across the entire network.
2Reliability
If traditional adversarial training is used to defend against adversarial attacks, then the neural network's robustness is improved, but the computational resources and complexity required for training increase
Solution Approach 1:
The patent segments the neural network's activation paths into vulnerable and non-vulnerable categories based on activation path differentials. This segmentation allows the training process to focus computational resources only on the vulnerable segments, reducing overall training complexity while maintaining effectiveness against adversarial attacks.
Solution Approach 2:
The patent introduces activation path differential as a new parameter to guide adversarial training. By computing and utilizing this differential parameter, the method dynamically adjusts training focus based on identified vulnerabilities, reducing the need for extensive hyperparameter tuning and simplifying the training process while improving robustness.
3Reliability
If traditional adversarial training is used, then some level of defense against adversarial examples is achieved, but classification accuracy deteriorates due to accuracy loss
Solution Approach 1:
The patent applies local quality by preserving the original training distribution for non-vulnerable activation paths while applying adversarial training adjustments only to vulnerable paths. This selective approach maintains high classification accuracy for normal inputs that traverse non-vulnerable paths while improving defense capability against adversarial attacks that exploit vulnerable paths.
Data Source
AI summary
In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.