A robust framework for tamper detection in digital recorded voice signals

Adriana Bernal-Patiño; Volodymyr I. Ponomaryov; Rogelio Reyes-Reyes; Clara Cruz-Ramos

doi:10.1117/12.2518495

14 May 2019 A robust framework for tamper detection in digital recorded voice signals

Adriana Bernal-Patiño, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos

Author Affiliations +

Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 109960R (2019) https://doi.org/10.1117/12.2518495
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

This paper focuses on the tampering detection and pinpointing the manipulated region in digital recorded voice signals via blind semi-fragile watermark embedded in the frequency domain using the Fast Fourier Transform and Quantization Index Modulation-Dither Modulation. The proposed method has been evaluated with several male and female recorded voice signals. The designed method can provide non-audible perceptible difference between the original and the watermarked voice signals obtaining an average Signal-to-Noise Ratio of 44dB, providing resistance to various MP3 compression rates. The simulation results of the parallel implementation of the proposed technique on multicore central processing unit platform has shown efficient performance in a real-time environment.

Citation Download Citation

Adriana Bernal-Patiño, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, and Clara Cruz-Ramos "A robust framework for tamper detection in digital recorded voice signals ", Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960R (14 May 2019); https://doi.org/10.1117/12.2518495

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE