A Robust RELAX-Based Algorithm for Enhanced Electric Network Frequency Estimation

The Electric Network Frequency (ENF) is an essential forensic signature embedded in multimedia content. Its accurate estimation is of paramount importance. For this purpose, a relaxation (RELAX) algorithm is proposed for enhanced ENF estimation. The RELAX algorithm works by iteratively refining the frequency estimates based on cyclic minimization, starting with initial frequency and amplitude estimates. It then sequentially improves these estimates through an iterative process, adjusting the parameters until convergence is achieved. The ENF-WHU dataset is employed to demonstrate the algorithm’s efficacy, showing enhanced estimation through extensive evaluation compared to state-of-the-art methods. Paired t-tests on average mean square error confirm a statistically significant improvement. The proposed method makes a significant contribution by leveraging nonlinear spectral estimation and enhancing the reliability and accuracy of ENF estimation in forensic applications.