Adversarial Perturbation for Object Tracker Robustness Validation
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Solution Overview
Problem
Multiple object tracking systems, including those used in autonomous driving, are vulnerable to adversarial attacks that can trick the system into misidentifying objects, even when the object detector is robust.
Innovation Solution
A method and system for determining a perturbation that reduces the similarity between features extracted from an image and those stored in a tracking system, effectively tricking the system into believing a new object is present, thereby testing its robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the object detector is made robust against adversarial attacks, then the detection accuracy is improved, but the tracking system remains vulnerable to attacks on feature similarity
Solution Approach 1:
The patent segments the tracking vulnerability into specific feature types (appearance features, motion features, trajectory features) and applies targeted perturbation analysis to each segment. This allows identifying which specific feature segments are most susceptible to attacks, enabling selective strengthening of those segments while maintaining overall system robustness.
Solution Approach 2:
The patent performs preliminary perturbation analysis by generating adversarial perturbations before actual tracking operations. This preliminary action identifies potential attack vectors and vulnerabilities in advance, allowing the system to be pre-hardened against these specific attack types without affecting normal tracking performance.
2Object-affected harmful factors
If perturbation is applied to reduce feature similarity, then the ability to trick the tracker is improved, but the similarity measure becomes too small affecting tracking accuracy
Solution Approach 1:
The patent changes the parameters of the perturbation by exploring different perturbation magnitudes, frequencies, and patterns. By systematically varying these parameters, the method identifies the optimal perturbation strength that maximizes tracking deception while maintaining realistic appearance, ensuring the perturbed features remain within plausible ranges.
Solution Approach 2:
The patent maintains continuity by ensuring that perturbed features continue to represent valid object appearances rather than breaking the feature distribution entirely. This allows the adversarial examples to continuously deceive the tracker while maintaining enough similarity to pass basic validation checks.
3Reliability
If historical features are stored and used for tracking, then the tracking robustness is improved, but the system complexity increases
Solution Approach 1:
The patent extracts only the essential historical features that are most critical for tracking robustness, such as trajectory patterns and appearance templates, while discarding redundant information. This extraction approach maintains tracking robustness by preserving key temporal dependencies without storing the entire history, thereby reducing system complexity.
4Object-affected harmful factors
If perturbation parameters are optimized to minimize similarity, then the attack effectiveness is improved, but the computational cost increases
Solution Approach 1:
The patent applies partial optimization by focusing perturbation parameter optimization only on the most vulnerable feature dimensions rather than optimizing all parameters exhaustively. This partial action approach achieves sufficient attack effectiveness by targeting critical weaknesses while avoiding the computational burden of complete parameter optimization.
Data Source
AI summary
A system and method, in particular computer implemented method for determining a perturbation for attacking and/or validating an association tracker. The method includes providing digital image data that includes an object, determining with the digital image data a first feature that characterizes the object, providing in particular from a storage a second feature that characterizes a tracked object, determining the perturbation depending on a measure of a similarity between the first feature and the second feature.