Adversarial Confidence Detection for Autonomous Driving Models

Solution Overview

Problem

Existing visual driving systems are susceptible to adversarial attacks that cause machine learning models to generate incorrect predictions, leading to improper decisions in autonomous vehicles.

Innovation Solution

The system refines machine learning models by generating adversarial images, processing them to identify incorrect predictions, and using the refined models to detect and mitigate adversarial attacks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If machine learning models are used in visual driving systems, then the system can process sensor data and make driving decisions, but the system becomes susceptible to adversarial attacks that cause incorrect predictions

Engineering Contradiction:

Core Design Contradiction:

Extent of automationVSReliability

Solution Approach 1:

The system performs preliminary actions by generating adversarial examples during the training phase and incorporating them into the training dataset. This allows the machine learning model to learn defensive patterns before deployment, enabling it to recognize and resist adversarial attacks during actual operation without requiring real-time complex computations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention converts the harmful adversarial examples into beneficial training data. By generating adversarial images with perturbed visual attributes and using them to train the model, the system transforms potential attack vectors into teaching materials that enhance the model's robustness and ability to distinguish between genuine and adversarial inputs.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

2Reliability

If ensemble of multiple machine learning models is used to defend against adversarial attacks, then the system's robustness improves, but computational overhead and resource consumption increase

Engineering Contradiction:

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The invention extracts and isolates the adversarial detection functionality into a specialized component that works alongside the primary machine learning model. Rather than running multiple full models, the system uses a dedicated adversarial example generator and detector that focuses specifically on identifying perturbations, thereby reducing overall computational overhead while maintaining robustness.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250068960A1Refining machine learning models to mitigate adversarial attacks in autonomous systems and applications

Publication Date: 2025.02.27

AI summary

In various examples, a technique for processing sensor data includes generating, using a machine learning model and based on a first sensor data instance, a first set of confidences for a set of output types and a first adversarial confidence that represents a likelihood that the first sensor data instance is adversarial. The technique also includes determining that the first sensor data instance is adversarial based on the first adversarial confidence. The technique further includes transmitting a first indication that the first sensor data instance is adversarial to one or more downstream components such that the one or more downstream components perform one or more operations based at least on the indication.