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 Conference Paper Faculty Publications- Conference Papers 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 Varkey, Tojin; George, Jossy; Chanti, S. Title A name given to the resource Automated Detection of Deepfakes using Integrated AI and Computer Vision Strategies 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 2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WorldSUAS66815.2025.11199153" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WorldSUAS66815.2025.11199153</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022291507?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022291507?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 Varkey T., CHRIST (Deemed to Be University), India; George J., CHRIST (Deemed to Be University), India; Chanti S., CHRIST (Deemed to Be University), India Description An account of the resource Deepfakes, or artificial intelligence-generated fake videos, are becoming a greater concern for online information trust, personal privacy, and digital content security. This paper presents a straightforward and understandable technique for automatically identifying deepfakes in order to address this significant problem. The approach makes use of conventional computer vision and machine learning methods. The model examines manually produced visual cues such as eye distance, mouth movement, and head tilt in video footage. To increase accuracy, it employs a variety of classifier types, including Random Forest, Gradient Boosting, and a soft Voting Classifier. A method known as SMOTE was used to clean and balance the data, and categorical data was transformed into a format suitable for machine learning models. With an F1-score of 0.9802 and 98% accuracy, the results demonstrate that the Voting Classifier, which combines several models, works admirably while being straightforward and effective. This method makes detection successful and simple to comprehend while offering a helpful tool for swiftly identifying deepfakes, especially on systems with constrained resources. 2025 IEEE. Subject The topic of the resource Computer Vision; Deepfake Detections; Ensemble Learning; Gradient Boosting; Random Forest; SMOTE Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153925-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online