Adversarial Feature Learning for Privacy-Preserving Visual Recognition
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Solution Overview
Problem
Visual recognition systems face privacy risks due to frequent data transmissions, as customer data shared for training machine learning models can be misused or intercepted by adversaries, leading to vulnerabilities in deep neural networks against model inversion attacks.
Innovation Solution
An adversarial reconstruction learning framework is employed to learn privacy-preserving feature representations by training a decoder to reconstruct input images from latent features and an encoder to maximize reconstruction error, preventing data reconstruction while maintaining utility performance, using a non-transitory computer-readable storage medium and system with processors to simulate a black-box model inversion attack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is transmitted frequently for training ML models, then model performance is improved, but privacy security deteriorates
Solution Approach 1:
The patent extracts only the necessary feature representations from the original images for model training, rather than transmitting the complete images. This extraction process removes personally identifiable visual information while retaining the essential characteristics needed for ML model performance, thereby improving privacy security without significantly compromising model training effectiveness.
Solution Approach 2:
The patent introduces an intermediary feature representation layer between the original images and the ML model. This intermediary representation acts as a mediator that preserves the essential information needed for model training while blocking the direct transmission of sensitive visual data, thus resolving the contradiction between model performance and privacy security.
2Productivity
If latent features are made invertible for utility tasks, then task performance is improved, but vulnerability to inversion attacks increases
Solution Approach 1:
The patent transforms the latent features through parameter changes that make them non-invertible. By applying transformations that alter the parameter space of the features, the system maintains the utility needed for task performance while ensuring that the transformed features cannot be easily inverted to reconstruct original images, thus reducing vulnerability to inversion attacks.
Solution Approach 2:
The patent converts the potential harm of invertible features into a benefit by deliberately designing non-invertible feature representations. The very property that could enable inversion attacks (preserved information) is transformed into a protective feature through controlled information loss and transformation, making the features useful for tasks while inherently resistant to inversion.
Data Source
AI summary
A method for protecting visual private data by preventing data reconstruction from latent representations of deep networks is presented. The method includes obtaining latent features from an input image and learning, via an adversarial reconstruction learning framework, privacy-preserving feature representations to maintain utility performance and prevent the data reconstruction by simulating a black-box model inversion attack by training a decoder to reconstruct the input image from the latent features and training an encoder to maximize a reconstruction error to prevent the decoder from inverting the latent features while minimizing the task loss.