Machine Learning Model Robustness Against Adversarial Attacks
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Solution Overview
Problem
The limited variety of software systems, such as machine-learned models, makes them vulnerable to adversarial attacks, where a successful attack on one model can affect a large number of devices, as the same models are deployed across multiple devices, leading to significant security risks.
Innovation Solution
Generating multiple, functionally equivalent variants of model subcomponents and combining them to create diverse machine-learned models, which are then deployed across different clients, increasing the variety of the software system and reducing vulnerability to adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same machine-learned model is deployed across multiple devices to achieve efficiency and consistency, then device compatibility and operational efficiency are improved, but vulnerability to adversarial attacks increases because a single attack can affect all deployed devices
Solution Approach 1:
The patent segments the machine-learned model into multiple subcomponents (e.g., convolutional layers, fully connected layers, activation functions). Each subcomponent has multiple functionally equivalent variants with different internal implementations. This segmentation allows creating diverse model variants that maintain the same overall functionality while having different vulnerability profiles to adversarial attacks.
Solution Approach 2:
The patent applies local quality by making different parts of the model (subcomponents) have different properties through using varied implementations. Each subcomponent variant maintains functional equivalence but differs in local characteristics such as weight initialization, layer architecture details, or activation function parameters. This local variation prevents adversarial examples from exploiting uniform vulnerabilities across all deployed models.
2Reliability
If multiple variants of model subcomponents are generated and combined to create diverse model variants, then robustness against adversarial attacks is improved, but system complexity increases
Solution Approach 1:
The model is divided into discrete subcomponents that can be independently varied. This segmentation makes the complexity manageable by allowing systematic combination of variants rather than creating entirely different models. Each subcomponent has a limited set of variants, and the overall system complexity is controlled through the structured combination approach.
Solution Approach 2:
The patent creates a universal framework where multiple subcomponent variants serve the same functional purpose. Different variants of the same subcomponent type can be interchanged without changing the overall model architecture or deployment infrastructure. This multi-functionality approach allows the system to handle complexity through standardized interfaces and modular replacement rather than requiring entirely separate systems.
3Object-affected harmful factors
If diverse model variants are deployed across different clients, then the impact of adversarial attacks is reduced, but deployment and management complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-generating multiple subcomponent variants and pre-combining them into model variants before deployment. This allows the diversity to be established in advance, and the selection of specific variants for deployment can be done systematically. The preliminary preparation of variants simplifies the deployment process compared to creating models on-demand.
Solution Approach 2:
The patent uses copying by creating multiple copies of model variants with different subcomponent combinations. These copies can be distributed to different clients or deployed in different environments. The copying approach allows efficient replication of the diverse model portfolio without requiring complex generation processes at deployment time, and enables easy updates by replicating modified variants.
Data Source
AI summary
The present disclosure provides systems and methods that reduce vulnerability of software systems (e.g., machine-learned models) to adversarial attacks by increasing variety within the software system. In particular, a software system can include a number of subcomponents that interoperate using predefined interfaces. To increase variety within the software system, multiple, different versions of one or more of the subcomponents of the software system can be generated. In particular, the different versions of the subcomponent(s) can be different from each other in some way, while still remaining functionally equivalent (e.g., able to perform the same functions with comparable accuracy/success). A plurality of different variants of the software system can be constructed by mixing and matching different versions of the subcomponents. A large amount of variety can be exhibited by the variants of the software system deployed at a given time, thereby leading to increased robustness against adversarial attacks.