Projected Vector Modification for Machine Learning Model Security
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Solution Overview
Problem
Machine learning models are vulnerable to adversarial techniques such as score fuzzing and string stuffing, which allow malicious actors to manipulate input data and cause misclassification, leading to potential security breaches.
Innovation Solution
The approach involves extracting features from an artifact, reducing them using feature reduction operations like PCA or random projection matrices, and attenuating the contribution of specific features that disproportionately influence the classification score, thereby preventing model manipulation and ensuring accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used for classification, then classification capability is improved, but vulnerability to adversarial manipulation increases
Solution Approach 1:
The patent introduces an intermediary component between the input artifact and the classification model that analyzes feature contributions and attenuates adversarial features. This intermediary layer processes the raw features before they reach the classification model, filtering out manipulated components while preserving legitimate classification signals, thus resolving the contradiction between maintaining classification capability and resisting manipulation.
Solution Approach 2:
The system implements feedback by analyzing the contribution of each feature to the classification score and using this information to attenuate suspicious features. The feedback loop continuously monitors feature influence and adjusts the weighting accordingly, allowing the system to adapt to adversarial manipulations while maintaining accurate classification of legitimate inputs.
2Productivity
If feature reduction operations are applied, then processing efficiency is improved, but information loss may occur
Solution Approach 1:
The patent applies parameter changes by transforming the feature space through reduction operations such as PCA or random projection matrices. These operations change the parameters (dimensions) of the feature representation, reducing complexity and improving processing efficiency while preserving the essential information needed for accurate classification and adversarial detection.
3Reliability
If contribution attenuation of specific features is performed, then resistance to adversarial attacks is improved, but classification accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by selectively attenuating only those features that exhibit characteristics of adversarial manipulation, while leaving legitimate features unchanged. This localized adjustment ensures that classification accuracy is maintained for benign inputs while resistance to manipulation is improved for adversarial inputs, resolving the contradiction between these two objectives.
Data Source
AI summary
An artifact is received from which features are extracted so as to populate a vector. The features in the vector can be reduced using a feature reduction operations to result in a modified vector having a plurality of buckets. A presence of predetermined types of features are identified within buckets of the modified vector influencing a score above a pre-determined threshold. A contribution of the identified features within the high influence buckets of the modified vector is then attenuated. The modified vector is input into a classification model to generate a score which can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.