Encoding Optical Element for Privacy-Preserving Computer Vision
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Solution Overview
Problem
Conventional cameras capture high-fidelity images that can infringe on privacy, and existing solutions fail to effectively obscure private information while maintaining functionality for computer vision tasks.
Innovation Solution
A system that uses an encoding optical element with a focal plane array detector and a processor-driven image processing pipeline to generate optimal distortions in images, learned via end-to-end optimization, which obscures privacy attributes while preserving features necessary for computer vision tasks, utilizing a deep neural network and optical aberrations to produce privacy-preserving images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cameras capture high-fidelity images, then image quality is improved, but privacy is compromised
Solution Approach 1:
The patent applies preliminary action by introducing optical distortions at the moment of image capture through specially designed lens elements. Rather than attempting to protect privacy after the image is captured, the system proactively degrades the image quality at the optical level before the harmful information can be recorded, thus preventing privacy infringement while maintaining the image's utility for computer vision tasks
Solution Approach 2:
The patent implements local quality by applying different distortion characteristics to different regions or aspects of the image. The optical elements are designed to selectively obscure specific privacy-sensitive features while preserving other important visual information needed for computer vision tasks, creating a non-uniform distortion pattern that targets harmful information locally rather than uniformly degrading the entire image
2Object-affected harmful factors
If optical distortions are applied to obscure privacy attributes, then privacy protection is improved, but computer vision task performance deteriorates
Solution Approach 1:
The patent applies parameter changes by systematically varying the distortion parameters of the optical elements to find an optimal balance. The system adjusts parameters such as distortion strength, type, and distribution to achieve sufficient privacy protection while maintaining adequate image quality for computer vision tasks. This involves tuning optical parameters like lens curvature, refractive indices, and element positioning to optimize the trade-off between privacy and functionality
Solution Approach 2:
The patent converts the harmful effect of image degradation into a benefit by designing the distortions specifically to protect privacy while preserving computer vision task performance. The optical distortions that would normally be considered harmful to image quality are deliberately engineered to obscure only privacy-sensitive information, thereby transforming what would be a detrimental effect into a protective mechanism that maintains both privacy and functionality
3Reliability
If end-to-end learning optimization is performed to balance privacy and task performance, then system effectiveness is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex iterative computational optimization with a pre-computed optical design. Instead of performing heavy end-to-end learning optimization during runtime or image processing, the system uses optical design software to pre-optimize the lens parameters offline. The resulting optical element design is then implemented in hardware, substituting computational complexity with a fixed optical configuration that achieves the desired privacy-protection and task-performance balance without requiring complex real-time computation
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
The system effectively protects privacy by generating distorted images that are difficult to deblur or enhance, while still allowing for high-performance computer vision tasks like human pose estimation, thus balancing privacy preservation with task performance.
Implementation Method 1
The encoding optical element includes an ensemble of optical elements that generate image distortion via light modulation
Implementation Method 2
The set of parameters for the encoding optical element add optical aberrations to the camera
Data Source
AI summary
Systems and methods for privacy-preserving optics are described. An embodiments includes a method of preserving-privacy on captured images while performing a computer vision task that includes generating an optimal set of parameters to parameterize an encoding optical element to produce optical distortions such that acquired images by the camera are distorted, where the optimal set of parameters is learned via end-to-end learning that jointly optimizes from a camera optics model to a computational process that performs a computer vision task on the distorted images acquired by the camera, where the distorted images visually obscure a privacy attribute of people to protect their privacy but still preserve features to perform the computer vision task, acquiring several distorted images, and performing a computer vision task directly on the distorted images where the distortions generated by the camera are optimal and allow obtaining high performance on the computer vision task.