The advancement of deepfake technologies, leveraging sophisticated artificial intelligence methods, poses substantial cybersecurity risks including misinformation dissemination, identity manipulation, and political propaganda. To address these challenges, this research introduces a novel Convolutional Neural Network (CNN)-based deep learning model named X-FACTS (eXplainable Facial Artifact and Consistency Tracking System). The proposed model incorporates explainable AI (XAI), adversarial training, and frequency-domain analysis techniques to enhance deepfake video detection capabilities in comparative analyses against established deepfake detection algorithms such as SHAP-based, LIME-based, Grad-CAM-based, and Mul-ti-Stream Frequency-based models. The X-FACTS algorithm consistently demonstrated superior performance, achieving higher accuracy (92.3%), accuracy (0.91), precision (0.94), recall (0.92), F1-score (0.91), and specificity (0.89). The results indicate that integrating CNN architecture with explainability frameworks significantly improves the identification of artificially generated content. The study concludes that robust, transparent, and explainable deep learning approaches like X-FACTS are essential to effectively combat emerging AI-driven misinformation threats, emphasizing the need for interdisciplinary cooperation to build resilient digital media forensic tools.
References
[1]
Suratkar, S., Kazi, F., Sakhalkar, M., Abhyankar, N. and Kshirsagar, M. (2020) Ex-posing Deepfakes Using Convolutional Neural Networks and Transfer Learning Approaches. 2020 IEEE 17th India Council International Conference (INDICON), New Delhi, 10-13 December 2020, 1-8.
[2]
Coccomini, D.A., Caldelli, R., Falchi, F., Gennaro, C. and Amato, G. (2022) Cross-forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection. Proceedings of the 1st International Workshop on Multimedia AI against Disinformation, Newark, 27-30 June 2022, 52-58. https://doi.org/10.1145/3512732.3533582
[3]
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014) Generative Adversarial Networks. arXiv: 1406.2661. https://arxiv.org/abs/1406.2661
[4]
Kietzmann, J., Lee, L.W., McCarthy, I.P. and Kietzmann, T.C. (2020) Deepfakes: Trick or Treat? Business Horizons, 63, 135-146. https://doi.org/10.1016/j.bushor.2019.11.006
[5]
Millière, R. (2022) Deep Learning and Synthetic Media. Synthese, 200, Article No. 231. https://doi.org/10.1007/s11229-022-03739-2
[6]
Dufour, N., Pathak, A., Samangouei, P., Hariri, N., Deshetti, S., Dudfield, A., Guess, C., Hernández Escayola, P., Tran, B., Babakar, M. and Bregler, C. (2024) AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild. arXiv: 2405.11697. https://arxiv.org/abs/2405.11697
[7]
Zhang, Y. and Wang, X. (2023) Deepfake Detection: A Comprehensive Survey from the Reliability Perspective. arXiv: 2211.10881. https://arxiv.org/abs/2211.10881
[8]
Gong, L.Y. and Li, X.J. (2024) A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges. Electronics, 13, Article 585. https://doi.org/10.3390/electronics13030585
[9]
Igba, E., Olarinoye, H.S., Nwakaego, V.E., Sehemba, D.B., Oluhaiyero, Y.S. and Okika, N. (2025) Synthetic Data Generation Using Generative AI to Combat Identity Fraud and Enhance Global Financial Cybersecurity Frameworks. International Journal of Scientific Research and Modern Technology (IJSRMT), 4, 1-19. https://doi.org/10.5281/zenodo.14928919
[10]
Idoko, I.P., Igbede, M.A., Manuel, H.N.N., Adeoye, T.O., Akpa, F.A. and Ukaegbu, C. (2024) Big Data and AI in Employment: The Dual Challenge of Workforce Replacement and Protecting Customer Privacy in Biometric Data Usage. Global Journal of Engineering and Technology Advances, 19, 89-106. https://doi.org/10.30574/gjeta.2024.19.2.0080
[11]
Ijiga, O.M., Idoko, I.P., Ebiega, G.I., Olajide, F.I., Olatunde, T.I. and Ukaegbu, C. (2024) Harnessing Adversarial Machine Learning for Advanced Threat Detection: Ai-Driven Strategies in Cybersecurity Risk Assessment and Fraud Prevention. Open Access Research Journal of Science and Technology, 11, 1-4. https://doi.org/10.53022/oarjst.2024.11.1.0060
[12]
Jinadu, S.O., Akinleye, E.A., Onwusi, C.N., Raphael, F.O., Ijiga, O.M. and Enyejo, L.A. (2023) Engineering Atmospheric CO2 Utilization Strategies for Revitalizing Mature American Oil Fields and Creating Economic Resilience. Engineering Science & Technology Journal Fair East Publishers, 4, 741-760.
[13]
Croitoru, F.A., Hiji, A.I., Hondru, V., Ristea, N.C., Irofti, P., Popescu, M., Rusu, C., Ionescu, R.T., Khan, F.S. and Shah, M. (2024) Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook. arXiv: 2411.19537. https://arxiv.org/abs/2411.19537
[14]
Akinleye, K.E., Jinadu, S.O., Onwusi, C.N., Omachi, A. and Ijiga, O.M. (2023) Integrating Smart Drilling Technologies with Real-Time Logging Systems for Maximizing Horizontal Wellbore Placement Precision. International Journal of Scientific Research in Science, Engineering and Technology, 11, 466-484. https://doi.org/10.32628/ijsrst2411429
[15]
Onuh, P., Ejiga, J.O., Abah, E.O., Onuh, J.O., Idogho, C. and Omale, J. (2024) Challenges and Opportunities in Nigeria’s Renewable Energy Policy and Legislation. World Journal of Advanced Research and Reviews, 23, 2354-2372. https://doi.org/10.30574/wjarr.2024.23.2.2391
[16]
Maduabuchi, C., Nsude, C., Eneh, C., Eke, E., Okoli, K., Okpara, E., et al. (2023) Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms. Energies, 16, Article 1603. https://doi.org/10.3390/en16041603
[17]
Echezona, U., Emmanuel, I. and Toyosi Motilol, O. (2024) Analyzing Edge AI Deployment Challenges within Hybrid IT Systems Utilizing Containerization and Blockchain-Based Data Provenance Solutions. International Journal of Scientific Research and Modern Technology, 3, 125-141. https://doi.org/10.38124/ijsrmt.v3i12.408
[18]
Permata, A.N.S., Idogho, C., Harsito, C., Thomas, I. and John, A.E. (2025) Compatibility in Thermoelectric Material Synthesis and Thermal Transport. Unconventional Resources, 7, Article ID: 100198. https://doi.org/10.1016/j.uncres.2025.100198
[19]
Khan, S.A., Artusi, A. and Dai, H. (2021) Adversarially Robust Deepfake Media Detection Using Fused Convolutional Neural Network Predictions. arXiv: 2102.05950. https://arxiv.org/abs/2102.05950
[20]
Pellicer, A., Bescos, B. and Arroyo, R. (2024) PUDD: Towards Robust Multi-Modal Prototype-Based Deepfake Detection. arXiv: 2406.15921. https://arxiv.org/abs/2406.15921
[21]
Nadimpalli, A.V. and Rattani, A. (2023) Facial Forgery-Based Deepfake Detection Using Fine-Grained Features. 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, 15-17 December 2023, 2174-2181. https://doi.org/10.1109/icmla58977.2023.00328
[22]
Tariq, S., Lee, S. and Woo, S. (2021) One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework. Proceedings of the Web Conference 2021, Ljubljana, 19-23 April 2021, 3625-3637. https://doi.org/10.1145/3442381.3449809
[23]
Yang, S., Guo, H., Hu, S., Zhu, B., Fu, Y., Lyu, S., Wu, X. and Wang, X. (2023) CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition. arXiv: 2310.00359. https://arxiv.org/abs/2310.00359
[24]
Rana, M.S. and Sung, A.H. (2024). Advanced Deepfake Detection Using Machine Learning Algorithms: A Statistical Analysis and Performance Comparison. 2024 7th International Conference on Information and Computer Technologies (ICICT), Honolulu, 15-17 March 2024, 75-81. https://doi.org/10.1109/icict62343.2024.00019
[25]
Guarnera, L., Giudice, O. and Battiato, S. (2020) Deepfake Detection by Analyzing Convolutional Traces. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, 14-19 June 2020, 2841-2850. https://doi.org/10.1109/cvprw50498.2020.00341
[26]
Ganiyusufoglu, I., Ng？, L.M., Savov, N., Karaoglu, S. And Gevers, T. (2020) Spatio-Temporal Features for Generalized Detection of Deepfake Videos. arXiv: 2010.11844. https://arxiv.org/abs/2010.11844
[27]
Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P. and AbdAlmageed, W. (2020) Two-Branch Recurrent Network for Isolating Deepfakes in Videos. In: Vedaldi, A., Bischof, H., Brox, T. and Frahm, J.M., Eds., Computer Vision—ECCV 2020, Springer, 667-684. https://doi.org/10.1007/978-3-030-58571-6_39
[28]
Mankar, A. and Selvakumar, S. (2022) Deepfake Detection Using Deep Learning Methods: A Systematic and Comprehensive Review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12, e1520. https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1520
[29]
Okika, N., Nwatuzie, G.A., Odozor, L., Oni, O. and Idoko, I.P. (2025) Addressing IoT-Driven Cybersecurity Risks in Critical Infrastructure to Safeguard Public Utilities and Prevent Large-Scale Service Disruptions. International Journal of Innovative Science and Research Technology, 10, 1333-1350. https://doi.org/10.5281/zenodo.14964285
[30]
Aloke, E.J. and Abah, J. (2023) Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes. International Journal of Applied Information Systems, 12, 1-14. https://doi.org/10.5120/ijais2023451952
[31]
Solaiyappan, S. and Wen, Y. (2022) Machine Learning Based Medical Image Deepfake Detection: A Comparative Study. Machine Learning with Applications, 8, Article ID: 100298. https://doi.org/10.1016/j.mlwa.2022.100298
[32]
Uzoma, E., Enyejo, J.O. and Motilola Olola, T. (2025) A Comprehensive Review of Multi-Cloud Distributed Ledger Integration for Enhancing Data Integrity and Transactional Security. International Journal of Innovative Science and Research Technology, 10, 1953-1970. https://doi.org/10.38124/ijisrt/25mar1970
[33]
Coccomini, D., Messina, N., Gennaro, C. and Falchi, F. (2021) Combining Efficient-Net and Vision Transformers for Video Deepfake Detection. arXiv:2107.02612. https://arxiv.org/abs/2107.02612
[34]
Heo, Y.J., Choi, Y.J., Lee, Y.W. an Kim, B.G. (2021) Deepfake Detection Scheme Based on Vision Transformer and Distillation. arXiv: 2104.01353. https://arxiv.org/abs/2104.01353
[35]
Ge, W., Patino, J., Todisco, M. and Evans, N. (2021) Explaining Deep Learning Models for Spoofing and Deepfake Detection with Shapley Additive Explanations. arXiv: 2110.03309. https://arxiv.org/abs/2110.03309
[36]
Ge, W., Todisco, M. and Evans, N. (2022) Explainable Deepfake and Spoofing Detection: An Attack Analysis Using Shapley Additive Explanations. arXiv: 2202.13693. https://arxiv.org/abs/2202.13693
[37]
Pino, S., Carman, M.J. and Bestagini, P. (2021) What’s Wrong with This Video? Comparing Explainers for Deepfake Detection. arXiv: 2105.05902. https://arxiv.org/abs/2105.05902
[38]
Wahidul, M.I., Abdullah, Y., Islam, M.M. and Aziz, T. (2021) Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI. Intelligent Automation & Soft Computing, 35, 217-232.
[39]
Tsigos, K., Apostolidis, E., Baxevanakis, S., Papadopoulos, S. and Mezaris, V. (2024) Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection. 3rd ACM International Workshop on Multimedia AI against Disinformation, Phuket, 10-14 June 2024, 37-45. https://doi.org/10.1145/3643491.3660292
[40]
Mahmud, F., Abdullah, Y., Islam, M. and Aziz, T. (2023) Unmasking Deepfake Faces from Videos Using an Explainable Cost-Sensitive Deep Learning Approach. 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, 13-15 December 2023, 1-6. https://doi.org/10.1109/iccit60459.2023.10441026
[41]
Nadimpalli, A.V. and Rattani, A. (2023) GBDF: Gender Balanced Deepfake Dataset Towards Fair Deepfake Detection. In: Rousseau, J.J. and Kapralos, B., Eds., Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, Springer, 320-337. https://doi.org/10.1007/978-3-031-37742-6_25
[42]
Pande, V. (2023) Deepfake Detection with Explainable AI. GitHub Repository. https://github.com/vikrampande7/deepfake-detection
[43]
Zhai, T., Lu, K., Li, J., Wang, Y., Zhang, W., Yu, P., et al. (2024) Learning Spatial‐frequency Interaction for Generalizable Deepfake Detection. IET Image Processing, 18, 4666-4679. https://doi.org/10.1049/ipr2.13276
[44]
Liu, Q., Yang, F. and Wang, S. (2024) DeepFake Detection Method Based on Multi-Scale Interactive Dual-Stream Network. Journal of Visual Communication and Image Representation, 89, Article ID: 103690.
[45]
Okika, N. Okoh, O.F. and Etuk, E.E. (2025) Mitigating Insider Threats and Social Engineering Tactics in Advanced Persistent Threat Operations through Behavioral Analytics and Cybersecurity Training. International Journal of Advance Research Publication and Reviews, 2, 11-27.
[46]
Khan, S.A. and Dang-Nguyen, D.T. (2023) Deepfake Detection: A Comparative Analysis. arXiv: 2308.03471. https://arxiv.org/abs/2308.03471
[47]
Ijiga, O.M., Ifenatuora, G.P. and Olateju, M. (2021) Digital Storytelling as a Tool for Enhancing STEM Engagement: A Multimedia Approach to Science Communication in K-12 Education. International Journal of Multidisciplinary Research and Growth Evaluation, 2, 495-505. https://doi.org/10.54660/.ijmrge.2021.2.5.495-505
[48]
Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M. and Canton Ferrer, C. (2020) The DeepFake Detection Challenge (DFDC) Dataset. arXiv: 2006.07397. https://arxiv.org/abs/2006.07397
[49]
Zi, B., Chang, M., Chen, J., Ma, X. and Jiang, Y.G. (2021) WildDeepFake: A Challenging Real-World Dataset for Deepfake Detection. arXiv: 2101.01456. https://arxiv.org/abs/2101.01456
[50]
Aikins, S.A., Yeboah, F.A.B., Enyejo, L.A. and Kareem, L.A. (2025) The Role of Thermomechanical and Aeroelastic Optimization in FRP-Strengthened Structural Elements for High-Performance Aerospace and Civil Applications. International Journal of Scientific Research in Mechanical and Materials Engineering, 9, 35-65. https://doi.org/10.32628/ijsrmme25144
[51]
Ayoola, V.B., Audu, B.A., Boms, J.C., Ifoga, S.M., Mbanugo, O.J. and Ugochukwu, U.N. (2024) Integrating Industrial Hygiene in Hospice and Home-Based Palliative Care to Enhance Quality of Life for Respiratory and Immunocompromised Patients. IRE Journals, 8, 339-357.
[52]
Ayoola, V.B., Ugochukwu, U.N., Adeleke, I., Michael, C.I., Adewoye, M.B. and Adeyeye, Y. (2024) Generative AI-Driven Fraud Detection in Health Care Enhancing Data Loss Prevention and Cybersecurity Analytics for Real-Time Protection of Patient Records. International Journal of Scientific Research and Modern Technology (IJSRMT), 3, 89-107. https://doi.org/10.38124/ijsrmt.v3i11.112
[53]
Matthew Ebika, I., Oche Idoko, D., Efe, F., Lawrence Anebi, E., Otakwu, A. and Innocent Odeh, I. (2024) Utilizing Machine Learning for Predictive Maintenance of Climate-Resilient Highways through Integration of Advanced Asphalt Binders and Permeable Pavement Systems with IoT Technology. International Journal of Innovative Science and Research Technology (IJISRT), 9, 69-89. https://doi.org/10.38124/ijisrt/ijisrt24nov074
[54]
Enyejo, L.A., Adewoye, M.B. and Ugochukwu, U.N. (2024) Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10, 332-354. https://doi.org/10.32628/cseit24106185
[55]
Enyejo, J.O., Obani, O.Q., Afolabi, O., Igba, E. and Ibokette, A.I. (2024) Effect of Augmented Reality (AR) and Virtual Reality (VR) Experiences on Customer Engagement and Purchase Behavior in Retail Stores. Magna Scientia Advanced Research and Reviews, 11, 132-150. https://doi.org/10.30574/msarr.2024.11.2.0116
[56]
Ijiga, A.C., Igbede, M.A., Ukaegbu, C., Olatunde, T.I., Olajide, F.I. and Enyejo, L.A. (2024) Precision Healthcare Analytics: Integrating ML for Automated Image Interpretation, Disease Detection, and Prognosis Prediction. World Journal of Biology Pharmacy and Health Sciences, 18, 336-354. https://doi.org/10.30574/wjbphs.2024.18.1.0214
[57]
Ijiga, A.C., Olola, T.M., Enyejo, L.A., Akpa, F.A., Olatunde, T.I. and Olajide, F.I. (2024) Advanced Surveillance and Detection Systems Using Deep Learning to Combat Human Trafficking. Magna Scientia Advanced Research and Reviews, 11, 267-286. https://doi.org/10.30574/msarr.2024.11.1.0091
[58]
Ijiga, M.O., Olarinoye, H.S., Yeboah, F.A.B. and Okolo, J.N. (2025) Integrating Behavioral Science and Cyber Threat Intelligence (CTI) to Counter Advanced Persistent Threats (APTs) and Reduce Human-Enabled Security Breaches. International Journal of Scientific Research and Modern Technology, 4, 1-15. https://doi.org/10.38124/ijsrmt.v4i3.376
[59]
Nwatuzie, G.A., Enyejo, L.A. and Umeaku, C. (2025) Enhancing Cloud Data Security Using a Hybrid Encryption Framework Integrating AES, DES, and RC6 with File Splitting and Steganographic Key Management. International Journal of Innovative Science and Research Technology, 10, 1555-1569. https://doi.org/10.5281/zenodo.14792173
[60]
Nwatuzie, G.A., Ijiga, O.M., Idoko, I.P., Enyejo, L.A. and Ali, E.O. (2025) Design and Evaluation of a User-Centric Cryptographic Model Leveraging Hybrid Algorithms for Secure Cloud Storage and Data Integrity. American Journal of Innovation in Science and Engineering, 4, 49-65. https://doi.org/10.54536/ajise.v4i2.4482
[61]
Idogho, C., Abah, E.O., Onuhc, J.O., Harsito, C., Omenkaf, K., Samuel, A., et al. (2025) Machine Learning-Based Solar Photovoltaic Power Forecasting for Nigerian Regions. Energy Science & Engineering, 13, 1922-1934. https://doi.org/10.1002/ese3.70013
[62]
James, U.U., Ijiga, O.M. and Enyejo, L.A. (2024) AI-Powered Threat Intelligence for Proactive Risk Detection in 5G-Enabled Smart Healthcare Communication Networks. International Journal of Scientific Research and Modern Technology, 3, 125-140. https://doi.org/10.38124/ijsrmt.v3i11.679
[63]
Christian, I., Abah, E.O., Abel, J.E., Harsito, C., Omoniyi, M. and Boriwaye, T. (2025) Compatibility Study of Synthesized Materials for Thermal Transport in Thermoelectric Power Generation. American Journal of Innovation in Science and Engineering, 4, 49-63. https://doi.org/10.54536/ajise.v4i1.3948
[64]
Idoko, I.P., Ayodele, T.R., Abolarin, S.M. and Ewim, D.R.E. (2023) Maximizing the Cost Effectiveness of Electric Power Generation through the Integration of Distributed Generators: Wind, Hydro and Solar Power. Bulletin of the National Research Centre, 47, Article No. 166. https://doi.org/10.1186/s42269-023-01125-7
[65]
Ijiga, A.C., Peace, A.E., Idoko, I.P., Agbo, D.O., Harry, K.D., Ezebuka, C.I. and Ukatu, I.E. (2024) Ethical Considerations in Implementing Generative AI for Healthcare Supply Chain Optimization: A Cross-Country Analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 7, 48-63. https://doi.org/10.53771/ijbpsa.2024.7.1.0015
[66]
Ayoola, V.B., Idoko, I.P., Eromonsei, S.O., Afolabi, O., Apampa, A.R. and Oyebanji, O.S. (2024) The Role of Big Data and AI in Enhancing Biodiversity Conservation and Resource Management in the USA. World Journal of Advanced Research and Reviews, 23, 1851-1873. https://doi.org/10.30574/wjarr.2024.23.2.2350
[67]
Idoko, I.P., Ijiga, O.M., Enyejo, L.A., Akoh, O., Isenyo, G., et al. (2024) Integrating Superhumans and Synthetic Humans into the Internet of Things (IoT) and Ubiquitous Computing: Emerging Ai Applications and Their Relevance in the U.S. Context. Global Journal of Engineering and Technology Advances, 19, 6-36. https://doi.org/10.30574/gjeta.2024.19.1.0055
[68]
Idoko, I.P., Ijiga, O.M., Akoh, O., Agbo, D.O., Ugbane, S.I. and Umama, E.E. (2024) Empowering Sustainable Power Generation: The Vital Role of Power Electronics in California’s Renewable Energy Transformation. World Journal of Advanced Engineering Technology and Sciences, 11, 274-293. https://doi.org/10.30574/wjaets.2024.11.1.0058
[69]
Idoko, I.P., David-Olusa, A., Badu, S.G., Okereke, E.K., Agaba, J.A. and Bashiru, O. (2024) The Dual Impact of AI and Renewable Energy in Enhancing Medicine for Better Diagnostics, Drug Discovery, and Public Health. Magna Scientia Advanced Biology and Pharmacy, 12, 99-127. https://doi.org/10.30574/msabp.2024.12.2.0048
[70]
Idoko, I.P., Eniodunmo, O., Danso, M.O., Bashiru, O., Ijiga, O.M. and Manuel, H.N.N. (2024) Evaluating Benchmark Cheating and the Superiority of MAMBA over Transformers in Bayesian Neural Networks: An In-Depth Analysis of AI Performance. World Journal of Advanced Engineering Technology and Sciences, 12, 372-389. https://doi.org/10.30574/wjaets.2024.12.1.0254
[71]
Ijiga, O.M., Idoko, I.P., Enyejo, L.A., Akoh, O., Ugbane, S.I. and Ibokette, A.I. (2024) Harmonizing the Voices of AI: Exploring Generative Music Models, Voice Cloning, and Voice Transfer for Creative Expression. World Journal of Advanced Engineering Technology and Sciences, 11, 372-394. https://doi.org/10.30574/wjaets.2024.11.1.0072
[72]
Billboard (2023) Wyclef Jean Discusses Upcoming Reggae Album and Reflects on Career. Billboard. https://www.billboard.com/music/rb-hip-hop/wyclef-jean-reggae-album-interview-1235722370/
