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A new image steganography method is proposed for data hiding. This method uses least significant bit (LSB) insertion to hide a message in one of the facial features of a given image. The proposed technique chooses an image of a face from a dataset of 8-bit color images of head poses and performs facial recognition on the image to extract the Cartesian coordinates of the eyes, mouth, and nose. A facial feature is chosen at random and each bit of the binary representation of the message is hidden at the least significant bit in the pixels of the chosen facial feature. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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This content will become publicly available on July 1, 2026
Privacy-Preserving Face Recognition and Verification With Lensless Camera
Facial recognition technology is becoming increasingly ubiquitous nowadays. Facial recognition systems rely upon large amounts of facial image data. This raises serious privacy concerns since storing this facial data securely is challenging given the constant risk of data breaches or hacking. This paper proposes a privacy-preserving face recognition and verification system that works without compromising the user’s privacy. It utilizes sensor measurements captured by a lensless camera - FlatCam. These sensor measurements are visually unintelligible, preserving the user’s privacy. Our solution works without the knowledge of the camera sensor’s Point Spread Function and does not require image reconstruction at any stage. In order to perform face recognition without information on face images, we propose a Discrete Cosine Transform (DCT) domain sensor measurement learning scheme that can recognize faces without revealing face images. We compute a frequency domain representation by computing the DCT of the sensor measurement at multiple resolutions and then splitting the result into multiple subbands. The network trained using this DCT representation results in huge accuracy gains compared to the accuracy obtained after directly training with sensor measurement. In addition, we further enhance the security of the system by introducing pseudo-random noise at random DCT coefficient locations as a secret key in the proposed DCT representation. It is virtually impossible to recover the face images from the DCT representation without the knowledge of the camera parameters and the noise locations. We evaluated the proposed system on a real lensless camera dataset - the FlatCam Face dataset. Experimental results demonstrate the system is highly secure and can achieve a recognition accuracy of 93.97% while maintaining strong user privacy.
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- Award ID(s):
- 2133084
- PAR ID:
- 10642011
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Biometrics, Behavior, and Identity Science
- Volume:
- 7
- Issue:
- 3
- ISSN:
- 2637-6407
- Page Range / eLocation ID:
- 354 to 367
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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