Discriminative Component Analysis Enhanced Feature Fusion of Electrical Network Frequency for Digital Audio Tampering Detection

Research in the domain of digital audio tampering detection has advanced significantly with the use of Electrical Network Frequency (ENF) analysis, presenting notable benefits for crime prevention and the enhancement of judicial integrity. However, the existing methodologies, particularly those analyzing ENF phase and frequency, are impeded by data clutter, redundancy, and incompatibilities with standard classification algorithms, leading to decreased detection efficacy. This study proposes a novel methodology employing Discriminant Component Analysis (DCA) for the fusion of ENF features, aiming to address these issues directly. By analyzing the distinct characteristics of ENF phase and frequency spectra, our approach uses DCA to merge these features effectively. This fusion not only amplifies the correlation between the features of phase and frequency but also simplifies the feature space through efficient dimensionality reduction. Additionally, to bridge the gap with traditional classification methods, we introduce a cascaded deep random forest algorithm, designed for intricate representational learning of the fused features. This sequential processing enhances the precision of our classification model significantly. Experimental results on both the Carioca and New Spanish public datasets demonstrate that our approach surpasses current state-of-the-art methods in terms of accuracy and robustness, establishing its superiority in the field of digital audio tampering detection. By integrating the DCA algorithm to accentuate feature uniqueness and maximize inter-feature correlation, alongside advanced representational learning via the deep random forest algorithm, our methodology markedly improves the accuracy of digital audio tampering detection.