Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, B. Anil; Misra, Neeraj Kumar; Pathak, Nirupma; Ahmadpour, Seyed-Sajad; Krishnamoorthy, Murugaperumal; Shukla, Dhirendra Kumar; Patidar, Mukesh; Hakimi, Musawer Title A name given to the resource Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Results in Engineering;Volume;28;Issue;;Article No.;107513; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.rineng.2025.107513" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.rineng.2025.107513</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018590292?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018590292?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar B.A., Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research (VFSTR), (Deemed to be University), Off-campus Hyderabad, Telangana, India; Misra N.K., School of Electronics Engineering, VIT-AP University, Andhra Pradesh, Amaravathi, 522237, India; Pathak N., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Guntur District, Andhra Pradesh, Vaddeswaram, 522502, India; Ahmadpour S.-S., Department of Computer Engineering, Faculty of Engineering and Natural Science, Istanbul Atlas University, Istanbul, Turkey; Krishnamoorthy M., Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, 501218, India; Shukla D.K., School of Computer Science, Galgotias University, Greater Noida, India; Patidar M., Department of Computer Science &amp; Engineering, Parul Institute of Engineering &amp; Technology (Parul University) Vadodara, India; Hakimi M., Department of Computer Science, Samangan University, Samangan Province, Northeast Aybak, Afghanistan Description An account of the resource Deepfake detection has become a critical component of digital forensics and security, as manipulated images and videos increasingly threaten trust in visual media. However, existing methods often struggle with robustness under post-processing operations such as JPEG compression, Gaussian blur, scaling, and filtering, and with the growing diversity and realism of face image modification (FIM) forgeries. This work proposes CMNV2, a hybrid architecture that integrates MobileNetV2 with a custom CAFFE block to enhance feature extraction and classification accuracy. By adding five additional layers to a pre-trained structure, the model demonstrates superior resilience against complex real-world conditions and achieves 99.10% accuracy across multiple datasets, outperforming 13 baseline CNN models. The study trained and tested CMNV2 on 5,000 images (real and deepfake faces), using a combination of deep neural networks (DNNs), transfer learning (TL), and deep learning (DL) techniques. Compared to 13 CNN-based architectures, the proposed model achieved superior performance across some important evaluation metrics, including accuracy, precision, recall, F1-score, error rate, and computational efficiency. These results highlight hybrid CMNV2 as a robust and efficient solution for deepfake face detection and classification, with potential applications in security, healthcare, and education. 2025 Subject The topic of the resource Classification; Deep learning; Deep neural network; Detection; Hybrid CMNV2 model; Transfer learning Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 25901230; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online