Adversarial Light Patterns for ML Detection Disorientation
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Solution Overview
Problem
Machine learning-based detection systems, such as those used in visual recognition and autonomous vehicles, can be disoriented or misled by adversarial patterns projected by light sources, preventing detection or identification of objects, while also enhancing detection probabilities or preventing camera focus through dynamic patterns.
Innovation Solution
The use of processors associated with objects to generate and project adversarial patterns or dynamic patterns using light sources, such as spotlights, screens, or hologram projectors, to disorient machine learning models, enhance object detection, or prevent camera focus, by altering the perceived appearance or focal points of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial patterns are projected to disorient machine learning detection systems, then detection accuracy of the system deteriorates, but the reliability of the projection system improves
Solution Approach 1:
The system pre-generates and projects adversarial patterns before the machine learning detection system can accurately identify the target object. These patterns are designed in advance to counteract and disorient the detection algorithm, preventing accurate recognition before it can occur.
Solution Approach 2:
The projected adversarial patterns serve as an intermediary element between the target object and the machine learning detection system. This intermediary modifies the visual information in the camera's field of view, creating a distorted representation that prevents accurate detection while not physically contacting or altering the target object itself.
2Reliability
If dynamic patterns are projected to prevent camera focus, then image capture quality deteriorates, but the privacy protection improves
Solution Approach 1:
The system projects dynamic patterns that change over time, causing the camera's autofocus mechanism to continuously adjust and fail to lock onto a fixed focal point. The patterns may shift position, intensity, or shape dynamically, preventing the camera from achieving stable focus and capturing clear images.
Solution Approach 2:
The dynamic patterns employ periodic changes in their visual properties, such as alternating brightness, position, or shape at specific frequencies. This periodic action confuses the camera's autofocus algorithm, causing it to continuously track the changing pattern rather than focusing on the underlying target object, thereby preventing clear image capture.
Data Source
AI summary
Methods and systems fort operating one or more light sources to project adversarial patterns generated to disorient a machine learning based detection system, comprising generating one or more adversarial patterns configured to disorient the machine learning based detection system and operating one or more light sources configured to project one or more of the adversarial pattern(s) in association with the targeted object in order to disorient the machine learning based detection system.