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A Method to Study Adaptation to Left-Right Reversed Audition
Published on: October 29, 2018
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Reversible gender privacy enhancement via adversarial perturbations
Yiyi Xie1, Yuqian Zhou1, Tao Wang1
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Summary
This study introduces a new method to protect gender privacy in facial images using adversarial perturbations. The technique enhances privacy by preventing gender recognition and attribute inference while allowing original image recovery.
Area of Science:
- Computer Vision
- Machine Learning
- Privacy-Preserving Technologies
Background:
- Deep learning enables accurate gender extraction from faces, posing privacy risks.
- Current gender privacy methods are insufficient, allowing attribute inference and lacking reversibility.
Purpose of the Study:
- To propose a novel gender privacy protection scheme.
- To enhance gender privacy while ensuring image reversibility and identity preservation.
Main Methods:
- Utilized continuously optimized adversarial perturbations to thwart gender recognition.
- Concealed gender-related attributes to prevent inference.
- Incorporated an identity preservation constraint and a reversible image transformation for recovery.
Main Results:
- Demonstrated effective gender privacy protection against unauthorized classifiers.
- Successfully prevented gender inference from related attributes.
- Achieved high identity preservation and lossless recovery of original images.
Conclusions:
- The proposed scheme offers robust gender privacy protection.
- It effectively supports reversibility and identity preservation, addressing limitations of existing methods.
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