Comparative analysis of GAN-based fusion deep neural models for fake face detection
Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were tra...
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| Format: | Article |
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AIMS Press
2024-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024071https://www.aimspress.com/article/doi/10.3934/mbe.2024071 |
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| author | Musiri Kailasanathan Nallakaruppan Chiranji Lal Chowdhary SivaramaKrishnan Somayaji Himakshi Chaturvedi Sujatha. R Hafiz Tayyab Rauf Mohamed Sharaf |
| author_facet | Musiri Kailasanathan Nallakaruppan Chiranji Lal Chowdhary SivaramaKrishnan Somayaji Himakshi Chaturvedi Sujatha. R Hafiz Tayyab Rauf Mohamed Sharaf |
| author_sort | Musiri Kailasanathan Nallakaruppan |
| collection | DOAJ |
| description | Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation. The proposed work performs the comparative analysis of the three fusion models, which can be integrated with Generative Adversarial Networks (GAN) based on the performance evaluation. The Model-3, which contains the combination of DenseNet-201+ResNet-102+Xception, offers the highest accuracy of 0.9797, and the Model-2 with the combination of DenseNet-201+ResNet-50+Inception V3 offers the lowest loss value of 0.1146; both are suitable for the GAN integration. Additionally, the Model-1 performs admirably, with an accuracy of 0.9542 and a loss value of 0.1416. A second dataset was also tested where the proposed Model-3 provided maximum accuracy of 86.42% with a minimum loss of 0.4054. |
| first_indexed | 2024-03-08T04:52:49Z |
| format | Article |
| id | doaj.art-b131308425a842c3bfd49e3e3f859edf |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-08T04:52:49Z |
| publishDate | 2024-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b131308425a842c3bfd49e3e3f859edf2024-02-08T00:50:17ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-01-012111625164910.3934/mbe.2024071Comparative analysis of GAN-based fusion deep neural models for fake face detectionMusiri Kailasanathan Nallakaruppan0Chiranji Lal Chowdhary 1SivaramaKrishnan Somayaji2Himakshi Chaturvedi3Sujatha. R 4Hafiz Tayyab Rauf 5Mohamed Sharaf61. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India3. School of Electronics and Communication Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India4. Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK5. Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaFake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation. The proposed work performs the comparative analysis of the three fusion models, which can be integrated with Generative Adversarial Networks (GAN) based on the performance evaluation. The Model-3, which contains the combination of DenseNet-201+ResNet-102+Xception, offers the highest accuracy of 0.9797, and the Model-2 with the combination of DenseNet-201+ResNet-50+Inception V3 offers the lowest loss value of 0.1146; both are suitable for the GAN integration. Additionally, the Model-1 performs admirably, with an accuracy of 0.9542 and a loss value of 0.1416. A second dataset was also tested where the proposed Model-3 provided maximum accuracy of 86.42% with a minimum loss of 0.4054.https://www.aimspress.com/article/doi/10.3934/mbe.2024071https://www.aimspress.com/article/doi/10.3934/mbe.2024071ganinception v3resnet-50mobile net v2densenet-201vgg-16xception |
| spellingShingle | Musiri Kailasanathan Nallakaruppan Chiranji Lal Chowdhary SivaramaKrishnan Somayaji Himakshi Chaturvedi Sujatha. R Hafiz Tayyab Rauf Mohamed Sharaf Comparative analysis of GAN-based fusion deep neural models for fake face detection Mathematical Biosciences and Engineering gan inception v3 resnet-50 mobile net v2 densenet-201 vgg-16 xception |
| title | Comparative analysis of GAN-based fusion deep neural models for fake face detection |
| title_full | Comparative analysis of GAN-based fusion deep neural models for fake face detection |
| title_fullStr | Comparative analysis of GAN-based fusion deep neural models for fake face detection |
| title_full_unstemmed | Comparative analysis of GAN-based fusion deep neural models for fake face detection |
| title_short | Comparative analysis of GAN-based fusion deep neural models for fake face detection |
| title_sort | comparative analysis of gan based fusion deep neural models for fake face detection |
| topic | gan inception v3 resnet-50 mobile net v2 densenet-201 vgg-16 xception |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024071https://www.aimspress.com/article/doi/10.3934/mbe.2024071 |
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