Adversarial Pattern Articles for Disrupting Visual Object Tracking
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Solution Overview
Problem
Current automated visual tracking systems can be misled by adversarial images, but generating adversarial examples for image classification may not be sufficient to prevent tracking, as visual object tracking involves dynamic changes in the target's appearance and positioning, requiring a more effective method to disrupt these systems.
Innovation Solution
A system and method for producing adversarial articles that apply specifically designed patterns to disrupt automated visual tracking processes, using an input module to generate adversarial patterns based on the tracking system's characteristics, which are then applied to physical or virtual objects, causing the tracker to misinterpret the image and lose tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adversarial images are used to mislead automated visual tracking systems, then the system may misclassify objects, but the tracking system can still maintain tracking lock due to dynamic adaptation
Solution Approach 1:
The patent applies preliminary action by pre-processing the target object with adversarial patterns before tracking begins. The pattern is designed in advance to exploit specific vulnerabilities of the tracking algorithm, ensuring that when the tracker attempts to adapt to dynamic changes, it is already operating from a compromised baseline that prevents successful tracking lock
Solution Approach 2:
The patent employs parameter changes by modifying specific visual parameters of the target object through adversarial patterns. These patterns alter color, texture, or structural parameters in ways that are imperceptible to humans but cause significant errors in machine vision systems, thereby changing the tracking parameters enough to break the tracking lock while maintaining visual appearance
2Measurement precision
If minor modifications are made to source images to create adversarial examples, then image classification may be misled, but visual object tracking remains effective due to dynamic adaptation requirements
Solution Approach 1:
The system performs preliminary action by pre-calculating and applying adversarial patterns that are specifically tailored to the tracking algorithm's vulnerability to dynamic changes. The pattern is designed beforehand to ensure that as the target moves or changes appearance, the adversarial effect compounds rather than diminishes, preventing the tracker from adapting
Solution Approach 2:
The patent applies dynamics by creating adversarial patterns that are specifically designed to interact with the dynamic nature of video tracking. The patterns account for temporal changes, motion blur, and frame-to-frame variations, making the tracking system's adaptability work against it rather than for it
3Reliability
If adversarial patterns are applied to articles, then automated visual tracking is disrupted, but the article's original function and appearance are preserved
Solution Approach 1:
The patent applies local quality by placing adversarial patterns in specific local regions of the article that are critical for tracking identification. Rather than uniformly modifying the entire article, the adversarial pattern is concentrated in key areas that the tracking algorithm relies upon, such as high-contrast edges or distinctive features, thereby maximizing disruption while minimizing visual impact
Solution Approach 2:
The patent employs color changes by applying adversarial patterns that modify color properties of the article in ways that are subtle to human perception but significant to machine vision systems. The color modifications exploit color space vulnerabilities of the tracking algorithm, changing hues, saturations, or brightness in patterns that maintain natural appearance to humans while confusing the automated system
Data Source
AI summary
System and method for producing an adversarial article that may be used to disrupt an automated visual tracking process. An input module receives input related to a specific automated visual tracking process. Based on that input, a pattern-design module generates an adversarial pattern. The adversarial pattern may then be applied to an article, which may be any kind of physical or virtual object. The tracker's normal processing modes are disrupted when the tracker attempts to process an image containing the adversarial article(s). The tracker may be mounted on an autonomous vehicle, a mobile robot, or other mobile or stationary camera surveillance system.