Phase Mask Optical Component for Privacy-Preserving Action Recognition
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Solution Overview
Problem
Existing machine vision systems face challenges in preserving privacy while performing human action recognition, as they often rely on software-level processing of high-resolution videos, making them susceptible to privacy breaches and adversarial attacks.
Innovation Solution
The system employs an optical component with learned camera lens parameters and an adversarial neural network to distort images and obscure privacy attributes, while preserving features for human action recognition, using a phase mask and temporal similarity matrices to maintain temporal information and prevent privacy leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software-level processing is used for human action recognition, then recognition accuracy can be achieved, but privacy protection is compromised
Solution Approach 1:
The system performs preliminary privacy protection by distorting the video stream at the optical level before it reaches the processing stage. The phase mask is positioned in the optical path to pre-distort the captured images, ensuring privacy attributes are obscured before any software processing occurs. This preliminary optical distortion prevents privacy breaches while preserving action recognition capabilities.
Solution Approach 2:
The phase mask acts as an intermediary element in the optical path that mediates between the captured scene and the sensor. It selectively distorts the optical information to hide privacy-sensitive features (faces, skin color) while preserving motion and action-related information. This intermediary optical component enables both privacy protection and action recognition simultaneously.
2Measurement precision
If high-resolution video is captured for action recognition, then recognition accuracy improves, but privacy leakage risk increases
Solution Approach 1:
The phase mask applies different distortion characteristics to different regions of the video frame. Areas containing privacy-sensitive information (such as faces and skin) receive stronger distortion, while regions containing action information maintain better quality. This spatially varying distortion quality enables selective preservation of action features while obscuring privacy attributes.
Solution Approach 2:
The system changes the optical parameters of the imaging system by introducing a phase mask with specific phase modulation characteristics. This modifies the point spread function and optical transfer function of the system, transforming the captured image properties to hide privacy information while preserving motion dynamics needed for action recognition.
3Reliability
If optical distortion is applied to protect privacy, then privacy protection improves, but image quality for recognition may deteriorate
Solution Approach 1:
The phase mask applies partial distortion - enough to protect privacy attributes but not so much as to completely degrade image quality. The distortion strength is optimized to be sufficient for privacy protection while maintaining adequate information for action recognition. This partial action approach balances the competing requirements.
Solution Approach 2:
The distortion applied by the phase mask can be viewed as segmenting the image information into protected regions (privacy attributes) and preserved regions (action information). Different parts of the image receive different treatment, with privacy-sensitive areas heavily distorted and action-relevant areas less distorted, enabling simultaneous privacy protection and recognition accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides robust privacy protection directly in the camera hardware, ensuring that privacy-sensitive information is protected at the image acquisition stage, maintaining high video action recognition accuracy and resisting adversarial attacks.
Implementation Method 1
the optical component includes a phase mask between the two thin convex lenses. the phase mask visually distorts images to visually obscure at least one privacy attribute of a person
Data Source
AI summary
Systems and methods of capturing privacy protected images and performing machine vision tasks are described. An embodiment includes a system that includes an optical component and an image processing application configured to capture distorted video using the optical component, where the optical component includes a set of optimal camera lens parameters θ*o learned using machine learning, performing a machine vision task on the distorted video, where the machine vision task includes a set of optimal action recognition parameters θ*c learned using the machine learning, and generating a classification based on the machine vision task, where the machine learning is jointly trained to optimize the optical element and the machine vision task.