Discrete Input Transformation via Embedding-Based Smoothing
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Solution Overview
Problem
Existing machine learning models are susceptible to adversarial input attacks, particularly in discrete input scenarios, where traditional defense mechanisms like input preprocessing and model regularization are ineffective against sophisticated attacks, and adversarial training requires high-quality adversarial examples that are challenging to generate.
Innovation Solution
Adapt randomized smoothing techniques by embedding discrete input data into a continuous space, injecting random noise, and processing it through a transformation subnetwork to produce robust transformations, which can defend against adversarial attacks without the need for extensive adversarial training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is used to enhance robustness, then model reliability improves, but the complexity of data preparation and training increases due to the need for high-quality adversarial examples
Solution Approach 1:
The patent introduces an intermediary transformation function that converts discrete input data into a continuous representation space. This intermediary space allows for the application of randomized smoothing techniques, which generate robust predictions by averaging model outputs over perturbed continuous representations. This approach simplifies the training process compared to traditional adversarial training while maintaining robustness.
Solution Approach 2:
The patent changes the parameter space from discrete to continuous by embedding discrete inputs into a continuous representation. This enables the use of randomized smoothing with continuous perturbations, which is computationally more efficient and less complex than generating discrete adversarial examples. The continuous parameter space allows for smoother optimization and easier generation of robust predictions.
2Reliability
If traditional defense mechanisms like input preprocessing and model regularization are applied, then device complexity remains low, but reliability against sophisticated adversarial attacks deteriorates
Solution Approach 1:
The patent replaces traditional mechanical defense mechanisms (input preprocessing and regularization) with a probabilistic approach using randomized smoothing. Instead of relying on fixed preprocessing rules or regularization terms, the system uses random perturbations in a continuous embedding space to create robust predictions. This substitution provides more effective defense against sophisticated attacks while maintaining computational efficiency.
3Reliability
If randomized smoothing is applied to continuous data, then robustness improves, but the method cannot be directly applied to discrete input data
Solution Approach 1:
The patent uses an intermediary embedding function that maps discrete input data into a continuous representation space. This intermediary continuous space is where randomized smoothing is applied. The embedding function serves as a bridge, allowing the continuous randomized smoothing technique to be adapted for discrete data by transforming the discrete inputs into continuous representations before applying the smoothing operation.
Data Source
AI summary
Embodiments disclose a method and a system for robust transformation of a discrete input with a neural network. The neural network including an embedding subnetwork and a transformation subnetwork. The method comprises embedding the discrete input into a continuous space using the embedding subnetwork to produce a continuous embedding, injecting a set of random noises of a predetermined magnitude into the continuous embedding to produce a set of perturbed embeddings, processing each of the set of perturbed embeddings with the transformation subnetwork to produce a set of transformations, and outputting a combination of the set of transformations as the robust transformation of the discrete input.