Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data

Ankit Kumar Patel, Dewanshi Paul, Sarthak Giri, Sneha Chaudhary, Bikalpa Gautam

TL;DR

The paper proposes a biocryptosystem that derives a 32-byte AES key from facial features using Histogram of Oriented Gradients (HOG) and a Support Vector Machine (SVM) classifier, enabling AES-CBC encryption of diverse multimedia data. It presents an end-to-end pipeline for face enrollment, authentication, and key formation, followed by encryption and decryption of files. The approach is evaluated on 25 files across various formats using metrics such as correlation, Shannon entropy, normalized Hamming distance, and avalanche effect, reporting near-zero or negative correlations, high entropy, ~0.5 normalized Hamming distance, and substantial diffusion. The results support the feasibility of biometric-based keying for secure data protection, with discussions on potential improvements through deep learning and broader applications in secure communications and access control.

Abstract

Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryption Standard (AES). The proposed system creates a distinct 32-bit encryption key derived from facial features identified by Histogram of Oriented Gradients (HOG) and categorized using Support Vector Machines (SVM). HOG efficiently identifies edge-aligned facial features, even in dim lighting, ensuring that reliable biometric keys can be generated. This key is then used with AES to encrypt and decrypt a variety of data formats, such as text, audio, and video files. This encryption key, derived from an individual's distinctive facial traits, is exceedingly challenging for adversaries to reproduce or guess. The security and performance of the system have been validated through experiments using several metrics, including correlation analysis, Shannon entropy, normalized Hamming distance, and the avalanche effect on 25 different file types. Potential uses for the proposed system include secure file sharing, online transactions, and data archiving, making it a strong and trustworthy approach to safeguarding sensitive information by integrating the uniqueness of facial biometrics with the established security of AES encryption.

Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data

TL;DR

The paper proposes a biocryptosystem that derives a 32-byte AES key from facial features using Histogram of Oriented Gradients (HOG) and a Support Vector Machine (SVM) classifier, enabling AES-CBC encryption of diverse multimedia data. It presents an end-to-end pipeline for face enrollment, authentication, and key formation, followed by encryption and decryption of files. The approach is evaluated on 25 files across various formats using metrics such as correlation, Shannon entropy, normalized Hamming distance, and avalanche effect, reporting near-zero or negative correlations, high entropy, ~0.5 normalized Hamming distance, and substantial diffusion. The results support the feasibility of biometric-based keying for secure data protection, with discussions on potential improvements through deep learning and broader applications in secure communications and access control.

Abstract

Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryption Standard (AES). The proposed system creates a distinct 32-bit encryption key derived from facial features identified by Histogram of Oriented Gradients (HOG) and categorized using Support Vector Machines (SVM). HOG efficiently identifies edge-aligned facial features, even in dim lighting, ensuring that reliable biometric keys can be generated. This key is then used with AES to encrypt and decrypt a variety of data formats, such as text, audio, and video files. This encryption key, derived from an individual's distinctive facial traits, is exceedingly challenging for adversaries to reproduce or guess. The security and performance of the system have been validated through experiments using several metrics, including correlation analysis, Shannon entropy, normalized Hamming distance, and the avalanche effect on 25 different file types. Potential uses for the proposed system include secure file sharing, online transactions, and data archiving, making it a strong and trustworthy approach to safeguarding sensitive information by integrating the uniqueness of facial biometrics with the established security of AES encryption.

Paper Structure

This paper contains 15 sections, 5 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Survey
  3. Applied Methodology
  4. Face enrollment and storage in database
  5. Face authentication using stored database
  6. Key formation/generation using face encoding
  7. Bio-key based AES Encryption
  8. Bio-key based AES Decryption
  9. Results and Discussion
  10. Sample Data
  11. Correlation Analysis
  12. Shannon Entropy Analysis
  13. Normalized Hamming Distance
  14. Avalanche Effect Analysis
  15. Conclusion

Figures (2)

  • Figure 1: Face Authentication-Based File Security System Flow Diagram.
  • Figure 2: Classification of Sample Data Files into Media Categories.