In the contemporary digital age, the proliferation of deepfakes presents a
formidable challenge to the sanctity of information dissemination. Audio
deepfakes, in particular, can be deceptively realistic, posing significant
risks in misinformation campaigns. To address this threat, we introduce the
Multi-Feature Audio Authenticity Network (MFAAN), an advanced architecture
tailored for the detection of fabricated audio content. MFAAN incorporates
multiple parallel paths designed to harness the strengths of different audio
representations, including Mel-frequency cepstral coefficients (MFCC),
linear-frequency cepstral coefficients (LFCC), and Chroma Short Time Fourier
Transform (Chroma-STFT). By synergistically fusing these features, MFAAN
achieves a nuanced understanding of audio content, facilitating robust
differentiation between genuine and manipulated recordings. Preliminary
evaluations of MFAAN on two benchmark datasets, 'In-the-Wild' Audio Deepfake
Data and The Fake-or-Real Dataset, demonstrate its superior performance,
achieving accuracies of 98.93% and 94.47% respectively. Such results not only
underscore the efficacy of MFAAN but also highlight its potential as a pivotal
tool in the ongoing battle against deepfake audio content.

为了解决深度伪造音频内容的问题，该研究引入了多特征音频真实性网络 (MFAAN)，该网络结构利用 Mel 频率倒谱系数 (MFCC)、线性频率倒谱系数 (LFCC) 和色度短时傅立叶变换 (Chroma-STFT) 等多种音频表示方法，通过协同融合这些特征，能够准确鉴别真实和伪造的录音，初步评估显示其在两个基准数据集上表现出卓越的性能，实现了分别达到 98.93% 和 94.47% 的准确率，这不仅突显了 MFAAN 的功效，还凸显了它作为应对深度伪造音频内容的关键工具的潜力。