Audio Deepfake Detection by Using Machine and Deep Learning

Abstract

Contemporarydevelopmentsinartificial intelligence have transformed the landscape of synthetic speech technology, facilitating the creation of exceptionally convincing audio that replicates human vocal characteristics including intonation, pitch, and speaking patterns. These synthetic audio productions, commonly referredto as audio deepfakes, represent a dual-natured phenomenon with both beneficial and harmful implications. While offering valuable applications in medical treatment, accessibility solutions, educational tools, and creative industries, they simultaneously introduce substantial security concerns including financial fraud, identity deception, propaganda distribution, and digital attacks. The increasing exploitation of synthetic audio technology highlights the critical necessity for developing dependable identification mechanisms.Thisresearchexaminescontemporary scholarly work in audio deepfake identification, emphasizingcomputationallearningandneural network methodologies. We present a comprehensive analysis of prevalent feature extraction techniques, examine different identification architectures, and evaluate their comparative effectiveness. Additionally, we address fundamental obstacles including insufficient training data, cross-linguistic and synthesis method compatibility, model transparency issues, and resistance to acoustic interference. We conclude by outlining future researchpathwaysthatprioritizesystemscalability, domain flexibility, and transparent artificial intelligence solutions.

Introduction

Artificial speech synthesis has rapidly advanced due to modern computational intelligence techniques. Neural networks can now replicate human voices—including accent, tone, and speaking style—with such realism that synthetic audio becomes nearly indistinguishable from real speech. While originally developed for helpful purposes like supporting people with speech impairments and improving digital assistants, these technologies are increasingly misused. Deepfake voices have already been used to commit fraud, spread misinformation, and impersonate public figures. Because audio deepfakes lack the obvious visual clues seen in video deepfakes, detecting them requires highly sophisticated signal-analysis and machine learning methods.

Literature Review

Early research focused on traditional signal processing techniques that analyzed spectral or temporal distortions, but these were limited in generality. Machine learning then expanded the field by using engineered features like MFCCs, LFCCs, and classifiers such as SVMs or random forests. However, as voice synthesis improved, handcrafted features became insufficient.

Deep learning now dominates the field:

• CNNs detect local patterns in spectrograms.

• RNNs and LSTMs capture temporal relationships in speech.

• Hybrid CNN–LSTM models outperform individual architectures.

• Transformers with attention mechanisms offer strong classification results.

Even with these advances, models still struggle to detect unknown synthesis technologies and handle noisy real-world audio.

Feature Extraction for Deepfake Detection

Key features used to expose synthetic speech include:

• MFCCs: capture perceptual sound characteristics that generators struggle to reproduce.

• Spectrograms: highlight harmonic and transition inconsistencies.

• Constant-Q transforms: provide pitch-sensitive analysis.

• Bispectral features: reveal nonlinear distortions.

• LFCCs: widely used in spoofing detection challenges.

These feature extraction methods form the foundation for machine and deep learning detection systems.

Machine Learning and Deep Learning Approaches

Traditional ML models (SVMs, decision trees, random forests, gradient boosting) are computationally simple and interpretable but weak against advanced deepfake systems.

Deep learning approaches dominate because they model both:

• Spectral patterns (via CNNs)

• Temporal dynamics (via LSTMs/GRUs)

Hybrid CNN–LSTM frameworks often exceed 88% accuracy in controlled tests. Transformers and ensemble learning methods show promise but face challenges in consistency and generalization.

Methodology of the Survey

The survey systematically reviewed recent research from major digital libraries, focusing on work from the last five years. Studies were categorized into:

1. Feature extraction techniques

2. Machine learning methods

3. Deep learning architectures

Datasets such as ASVspoof were examined, along with evaluation metrics like accuracy, precision, recall, F1-score, and AUC. The review also considered cross-language detection, robustness to background noise, and adaptability to new synthesis methods.

Discussion

While audio deepfake detection has advanced significantly, several issues persist:

• Traditional ML fails against modern synthesizers.

• Deep learning works better but requires heavy computation.

• Models lack interpretability, limiting forensic and real-time use.

• Dataset diversity is insufficient for robust generalization.

• Detection performance drops in noisy or compressed environments.

Future research must prioritize:

• More diverse and realistic datasets

• Efficient and interpretable model designs

• Better resilience to evolving voice-generation technologies

Conclusion

The expansion of audio deepfakes constitutes both an advancement and a challenge in contemporary digital communication. While synthetic speech technologiesofferclearadvantagesinaccessibility and human-computer interaction, their malicious application in fraud, impersonation, and misinformation creates risks for individuals and communities. Investigation into audio deepfake detection has achieved significant advancement, with MFCC-based feature extraction and deep learning models reaching high detection precision in controlled conditions. Nevertheless, obstacles persistregardingdatasetdiversity,generalization capability, interpretability, and practicalrobustness. The future development of this field depends on creating scalable, transparent, andflexible solutions that can maintain pace with the continuous evolutionofgenerativetechnologies.Achievement inthis domain will not only enhancecybersecurity and forensics but will also serve a crucial function inpreservingconfidenceindigitalcommunication. As deepfakes become progressively more advanced, the creation of dependable detection systems is no longer discretionarybut essential for protecting digital authenticity.

References

[1] A. K. Singh and P. Singh, \"Detection of AI- synthesized speech using cepstral bispectral statistics,\" IEEE MIPR, 2021. [2] T. Arif et al., \"Voice spoofing countermeasure forlogicalaccess attacks detection,\" IEEEAccess, 2021. [3] D.Marietal.,\"Thesoundofsilence:Efficiency of first digit features in synthetic audio detection,\" Elsevier Signal Processing, 2022. [4] L. Cuccovillo et al., \"Open challenges in synthetic speech detection,\" ACM Workshop, 2022. [5] D. Salvi et al., \"Synthetic speech detection through audio folding,\" ACM, 2023.

Copyright

Copyright © 2025 Dr. Anitha G, S V Murali, Sanath U, Srinivas K T, Yeshwanth H K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.