Model-Agnostic Adversarial Attack Detection for AI Object Detectors
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Solution Overview
Problem
Existing methods for detecting adversarial attacks on AI-based object detectors require access to the model or image inputs, making them model-dependent and less effective for real-time applications, especially in scenarios like smart traffic systems where accuracy and reliability are critical.
Innovation Solution
A model-agnostic, real-time detection system using the Isolation Forest algorithm that identifies adversarial patch attacks solely based on the output of object detectors, without accessing the model or original images, by mapping normal attributes of bounding boxes and confidence vectors to calculate anomaly scores and mitigate attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection methods are used that require access to the model or image inputs, then detection capability is improved, but device complexity and real-time performance are worsened
Solution Approach 1:
The patent extracts only the necessary detection functionality from complex model-access methods, creating a standalone detector that operates on output data alone. This eliminates the need for model integration while maintaining detection capability through a simplified architecture that processes only bounding box and confidence score data.
Solution Approach 2:
The patent introduces an intermediary detection system that sits between the object detector and the application, analyzing output data without requiring access to the detector's internal model or input images. This intermediary layer enables attack detection while preserving the independence and simplicity of both the detector and application components.
2Measurement precision
If model-dependent detection methods are used, then detection accuracy is improved, but adaptability to different object detectors is worsened
Solution Approach 1:
The patent creates a universal detection system that can identify adversarial attacks across multiple different object detector architectures (YOLO, SSD, Faster R-CNN) using a single model-agnostic approach. The detector analyzes common output structures (bounding boxes and confidence scores) rather than architecture-specific features, enabling broad applicability without sacrificing detection accuracy.
Solution Approach 2:
Instead of adapting the detection method to each specific detector model, the patent inverts the approach by having the detector adapt to standard output formats from various models. This reversal enables a single detection system to serve multiple detector types without requiring custom integration for each model.
3Reliability
If existing attack detection methods are deployed, then security is improved, but processing speed and real-time performance are worsened
Solution Approach 1:
The patent employs a lightweight detection model that processes data quickly and discards results after each frame, enabling real-time operation. The simple architecture processes only essential output features without complex analysis, maintaining high processing speed while providing continuous security monitoring at video frame rates.
Solution Approach 2:
The patent performs partial analysis by focusing only on the most critical aspects of detector output (bounding box confidence scores and spatial relationships) rather than comprehensive model analysis. This selective approach provides sufficient security detection while maintaining real-time processing capability.
Data Source
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AI summary
An Al-based method for real-time detection and mitigation of attacks on object detectors being fed by input images acquired by one or more imagers, comprising the steps of mapping normal attributes of the outputs of an ML-model associated with the object detectors, using unsupervised learning; creating an anomaly detection model being capable of identifying adversarial attacks in the form of adversarial patchs, based solely on the outputs of the object detectors and without accessing the object detectors model or any original frames aquired by the one or more imagers; calculating the anomaly score for each object being detected by the ML-model object detectors; comparing the anomaly scores of the detected objects to a preset threshold; protecting the object detectors agaist the attacks by identifying and mitigating the effects of the adversarial patch attacks using the comparison results.