Model Ensembling Across Inputs and Layers for Adversarial Robustness
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Solution Overview
Problem
Machine learning models are vulnerable to adversarial attacks, which exploit subtle perturbations in input data to deceive the models into making incorrect decisions, posing significant challenges in ensuring the security and reliability of these systems, especially in critical applications.
Innovation Solution
Enhance adversarial robustness through ensembling across input resolutions, models, and/or model layers by transforming input images into augmented versions at different resolutions and stacking these variations, and aggregating predictions from multiple models or layers using techniques like CrossMax to mitigate outlier predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple augmented versions of input images are generated and stacked in the channel dimension, then adversarial robustness is improved, but computational complexity increases
Solution Approach 1:
The input image is segmented into multiple augmented versions with different transformations (rotations, flips, color jitter, etc.), which are then stacked along the channel dimension. This segmentation allows the model to process diverse views of the same input simultaneously, improving robustness against adversarial attacks while managing computational load through efficient parallel processing.
Solution Approach 2:
The patent transforms the input representation by stacking multiple augmented image versions along the channel dimension, effectively adding a new dimension to the data tensor. This dimensional transformation enables the model to leverage multi-view information without requiring architectural changes, thereby improving adversarial robustness with moderate computational overhead.
2Reliability
If ensembling across multiple models or model layers is performed, then prediction robustness is improved, but computational resources increase
Solution Approach 1:
The patent merges predictions from multiple sources including different augmented input versions, multiple model instances, and intermediate layer outputs through ensemble aggregation. By combining these diverse predictions with techniques like CrossMax, the system achieves improved robustness against adversarial attacks while sharing computational resources across the ensemble members.
Solution Approach 2:
The model performs self-ensembling by utilizing its own intermediate layer outputs as additional prediction sources. This self-service approach allows the model to generate multiple perspectives on the same input without requiring external ensemble members, thereby reducing overall computational resource requirements while maintaining prediction robustness.
3Adaptability or versatility
If augmented versions of input images are generated through resolution changes and stacking, then model adaptability is improved, but processing time increases
Solution Approach 1:
Multiple augmented versions of the input image are generated in advance before being passed to the model for processing. These augmentations include resolution changes, rotations, flips, and color transformations. By preparing these diverse input versions beforehand, the model becomes more adaptable to different input conditions while the preprocessing step manages the time cost through efficient image transformation operations.
Data Source
AI summary
Provided are systems and methods that enhance the adversarial robustness of machine learning models through the use of ensembling across input resolutions, models, and/or model layers. In particular, one aspect of the present disclosure is directed to the use of a multi-resolution input representation. The proposed multi-resolution input can be generated by transforming an input image into multiple resolutions and stacking these variations to enrich the model's input data. Additionally or alternatively to the multi-resolution approach, the present disclosure also provides ensemble aggregation methods that aggregate across multiple models and/or layers of a model. The ensemble aggregation methods can improve adversarial robustness by aggregating outputs from multiple models, model checkpoints, or even multiple intermediate layers from the same model in a manner that mitigates the influence of outlier predictions, such as de-correlated predictions generated by certain model(s) or by certain layer(s) of a model due to an adversarial attack.