Advancing deepfake detection using Xception architecture: A robust approach for safeguarding against fabricated news on social media
| dc.contributor.author | Alkurdi, Dunya Ahmed | |
| dc.contributor.author | Çevik, Mesut | |
| dc.contributor.author | Akgundogdu, Abdurrahim | |
| dc.date.accessioned | 2025-01-03T10:54:13Z | |
| dc.date.available | 2025-01-03T10:54:13Z | |
| dc.date.issued | 2024 | en_US |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant performance. With an ever-rising threat from fake media, this piece of research puts great emphasis on stringent memory testing to keep at bay the spread of manipulated content. It also justifies better explanation methods to justify the reasoning done by the model for those decisions that build more trust and reliability. The ensemble models being more accurate have been studied and examined for establishing a possibility of combining various detection frameworks that could together produce superior results. Further, the study underlines the need for real-time detection tools that can be effective on different social media sites and digital environments. Ethics, protecting privacy, and public awareness in the fight against the proliferation of deepfakes are important considerations. By significantly contributing to the advancements made in the technology that has actually advanced detection, it strengthens the safety and integrity of the cyber world with a robust defense against ever-evolving deepfake threats in technology. Overall, the findings generally go a long way to prove themselves as the crucial step forward to ensuring information authenticity and the trustworthiness of society in this digital world. | en_US |
| dc.identifier.citation | Alkurdi, D. A., Çevik, M., Akgündoğdu, A. (2024). Advancing deepfake detection using Xception architecture: A robust approach for safeguarding against fabricated news on social media. Computers, Materials and Continua, 81(3), 4285-4305. 10.32604/cmc.2024.057029 | en_US |
| dc.identifier.endpage | 4305 | en_US |
| dc.identifier.issn | 1546-2218 | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopus | 2-s2.0-85212784187 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 4285 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/5136 | |
| dc.identifier.volume | 81 | en_US |
| dc.identifier.wos | WOS:001385244800001 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Alkurdi, Dunya Ahmed | |
| dc.institutionauthor | Çevik, Mesut | |
| dc.language.iso | en | |
| dc.publisher | Tech Science Press | en_US |
| dc.relation.ispartof | Computers, Materials and Continua | |
| dc.relation.isversionof | 10.32604/cmc.2024.057029 | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrenci | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Data processing | en_US |
| dc.subject | Deepfake Detection | en_US |
| dc.subject | Image processing | en_US |
| dc.subject | Intelligent information systems | en_US |
| dc.subject | Social media security | en_US |
| dc.subject | Xception architecture | en_US |
| dc.title | Advancing deepfake detection using Xception architecture: A robust approach for safeguarding against fabricated news on social media | |
| dc.type | Article |
