Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments.
Deepfakes, synthetic media created using artificial intelligence, threaten the authenticity of digital content. Traditional detection methods, such as Convolutional Neural Networks (CNNs), require substantial computational resources, rendering them impractical for resource-constrained devices like s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326565 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849721148143566848 |
|---|---|
| author | Nadeem Jabbar Sohail Masood Bhatti Muhammad Rashid Arfan Jaffar Sheeraz Akram |
| author_facet | Nadeem Jabbar Sohail Masood Bhatti Muhammad Rashid Arfan Jaffar Sheeraz Akram |
| author_sort | Nadeem Jabbar |
| collection | DOAJ |
| description | Deepfakes, synthetic media created using artificial intelligence, threaten the authenticity of digital content. Traditional detection methods, such as Convolutional Neural Networks (CNNs), require substantial computational resources, rendering them impractical for resource-constrained devices like smartphones and IoT systems. This study evaluates a single-layer Kolmogorov-Arnold Network (KAN) with 200 nodes for efficient deepfake classification. Experimental results show that KAN achieves 95.01% accuracy on the FaceForensics++ dataset and 88.32% on the Celeb-DF dataset, while requiring only 52.4 MB of memory, 13.11 million parameters, and 26.21 million FLOPs, significantly less than state-of-the-art CNNs. These verified metrics highlight KAN's potential for real-time deepfake detection on edge devices. Untested capabilities, such as robustness against adversarial attacks, are proposed for future research. This work aligns with the United Nations Sustainable Development Goals, specifically SDG 9: Industry, Innovation, and Infrastructure and SDG 16: Peace, Justice, and Strong Institutions. |
| format | Article |
| id | doaj-art-9cd2883fc362469e9294ddce7492b9fb |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-9cd2883fc362469e9294ddce7492b9fb2025-08-20T03:11:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032656510.1371/journal.pone.0326565Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments.Nadeem JabbarSohail Masood BhattiMuhammad RashidArfan JaffarSheeraz AkramDeepfakes, synthetic media created using artificial intelligence, threaten the authenticity of digital content. Traditional detection methods, such as Convolutional Neural Networks (CNNs), require substantial computational resources, rendering them impractical for resource-constrained devices like smartphones and IoT systems. This study evaluates a single-layer Kolmogorov-Arnold Network (KAN) with 200 nodes for efficient deepfake classification. Experimental results show that KAN achieves 95.01% accuracy on the FaceForensics++ dataset and 88.32% on the Celeb-DF dataset, while requiring only 52.4 MB of memory, 13.11 million parameters, and 26.21 million FLOPs, significantly less than state-of-the-art CNNs. These verified metrics highlight KAN's potential for real-time deepfake detection on edge devices. Untested capabilities, such as robustness against adversarial attacks, are proposed for future research. This work aligns with the United Nations Sustainable Development Goals, specifically SDG 9: Industry, Innovation, and Infrastructure and SDG 16: Peace, Justice, and Strong Institutions.https://doi.org/10.1371/journal.pone.0326565 |
| spellingShingle | Nadeem Jabbar Sohail Masood Bhatti Muhammad Rashid Arfan Jaffar Sheeraz Akram Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. PLoS ONE |
| title | Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. |
| title_full | Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. |
| title_fullStr | Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. |
| title_full_unstemmed | Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. |
| title_short | Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained environments. |
| title_sort | single layer kan for deepfake classification balancing efficiency and performance in resource constrained environments |
| url | https://doi.org/10.1371/journal.pone.0326565 |
| work_keys_str_mv | AT nadeemjabbar singlelayerkanfordeepfakeclassificationbalancingefficiencyandperformanceinresourceconstrainedenvironments AT sohailmasoodbhatti singlelayerkanfordeepfakeclassificationbalancingefficiencyandperformanceinresourceconstrainedenvironments AT muhammadrashid singlelayerkanfordeepfakeclassificationbalancingefficiencyandperformanceinresourceconstrainedenvironments AT arfanjaffar singlelayerkanfordeepfakeclassificationbalancingefficiencyandperformanceinresourceconstrainedenvironments AT sheerazakram singlelayerkanfordeepfakeclassificationbalancingefficiencyandperformanceinresourceconstrainedenvironments |