{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:30:14Z","timestamp":1771065014164,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Generative adversarial networks (GANs) have revolutionised various fields by creating highly realistic images, videos, and audio, thus enhancing applications such as video game development and data augmentation. However, this technology has also given rise to deepfakes, which pose serious challenges due to their potential to create deceptive content. Thousands of media reports have informed us of such occurrences, highlighting the urgent need for reliable detection methods. This study addresses the issue by developing a deep learning (DL) model capable of distinguishing between real and fake face images generated by StyleGAN. Using a subset of the 140K real and fake face dataset, we explored five different models: a custom CNN, ResNet50, DenseNet121, MobileNet, and InceptionV3. We leveraged the pre-trained models to utilise their robust feature extraction and computational efficiency, which are essential for distinguishing between real and fake features. Through extensive experimentation with various dataset sizes, preprocessing techniques, and split ratios, we identified the optimal ones. The 20k_gan_8_1_1 dataset produced the best results, with MobileNet achieving a test accuracy of 98.5%, followed by InceptionV3 at 98.0%, DenseNet121 at 97.3%, ResNet50 at 96.1%, and the custom CNN at 86.2%. All of these models were trained on only 16,000 images and validated and tested on 2000 images each. The custom CNN model was built with a simpler architecture of two convolutional layers and, hence, lagged in accuracy due to its limited feature extraction capabilities compared with deeper networks. This research work also included the development of a user-friendly web interface that allows deepfake detection by uploading images. The web interface backend was developed using Flask, enabling real-time deepfake detection, allowing users to upload images for analysis and demonstrating a practical use for platforms in need of quick, user-friendly verification. This application demonstrates significant potential for practical applications, such as on social media platforms, where the model can help prevent the spread of fake content by flagging suspicious images for review. This study makes important contributions by comparing different deep learning models, including a custom CNN, to understand the balance between model complexity and accuracy in deepfake detection. It also identifies the best dataset setup that improves detection while keeping computational costs low. Additionally, it introduces a user-friendly web tool that allows real-time deepfake detection, making the research useful for social media moderation, security, and content verification. Nevertheless, identifying specific features of GAN-generated deepfakes remains challenging due to their high realism. Future works will aim to expand the dataset by using all 140,000 images, refine the custom CNN model to increase its accuracy, and incorporate more advanced techniques, such as Vision Transformers and diffusion models. The outcomes of this study contribute to the ongoing efforts to counteract the negative impacts of GAN-generated images.<\/jats:p>","DOI":"10.3390\/computers14020060","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T06:06:06Z","timestamp":1739340366000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using Deep Learning to Identify Deepfakes Created Using Generative Adversarial Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Jhanvi","family":"Jheelan","sequence":"first","affiliation":[{"name":"ICT Department, FoICDT, University of Mauritius, Reduit 80837, Mauritius"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7077-8681","authenticated-orcid":false,"given":"Sameerchand","family":"Pudaruth","sequence":"additional","affiliation":[{"name":"ICT Department, FoICDT, University of Mauritius, Reduit 80837, Mauritius"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the International Conference on Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Raza, A., Munir, K., and Almutairi, M. (2022). A Novel Deep Learning Approach for Deepfake Image Detection. Appl. Sci., 12.","DOI":"10.3390\/app12199820"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Walczyna, T., and Piotrowski, Z. (2024). Fast Fake: Easy-to-Train Face Swap Model. Appl. Sci., 14.","DOI":"10.20944\/preprints202402.0286.v1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hsu, C.-C., Zhuang, Y.-X., and Lee, C.-Y. (2020). Deep Fake Image Detection Based on Pairwise Learning. Appl. Sci., 10.","DOI":"10.3390\/app10010370"},{"key":"ref_5","unstructured":"Kondrashov, S. (2024, September 25). Reimagining Digital Creativity: The Impact of Deepfake Technology on Artistic Expression. Medium. Available online: https:\/\/medium.com\/@realstanislavkondrashov\/stanislav-kondrashov-explores-the-impact-of-deepfake-technology-on-artistic-expression-84aec4ca1d49."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Janut\u0117nas, L., Janut\u0117nait\u0117-Bogdanien\u0117, J., and \u0160e\u0161ok, D. (2023). Deep Learning Methods to Detect Image Falsification. Appl. Sci., 13.","DOI":"10.3390\/app13137694"},{"key":"ref_7","unstructured":"Clarke, M. (2024, July 23). Keeping It Real: How to Spot a Deepfake. CSIRO. Available online: https:\/\/www.csiro.au\/en\/news\/all\/articles\/2024\/february\/detect-deepfakes."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.18280\/ts.390330","article-title":"Deepfakes Classification of Faces Using Convolutional Neural Networks","volume":"39","author":"Sharma","year":"2022","journal-title":"Trait. Signal"},{"key":"ref_9","unstructured":"Xhlulu (2024, May 16). 140K Real and Fake Face. Available online: https:\/\/www.kaggle.com\/datasets\/xhlulu\/140k-real-and-fake-faces."},{"key":"ref_10","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Peri\u0161i\u0107, N., and Jovanovi\u0107, R. (2022). Convolutional Neural Networks for Real and Fake Face Classification. Sinteza 2022\u2014International Scientific Conference on Information Technology and Data Related Research 2022, Singidunum University.","DOI":"10.15308\/Sinteza-2022-29-35"},{"key":"ref_12","unstructured":"Cortuk, D. (2024, May 02). Generative Adversarial Networks (GANs): A Journey into AI-Generated Art. Medium. Available online: https:\/\/medium.com\/@derya.cortuk\/generative-adversarial-networks-gans-a-journey-into-ai-generated-art-7b7f9e40d4f5."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Naitali, A., Ridouani, M., Salahdine, F., and Kaabouch, N. (2023). Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions. Computers, 12.","DOI":"10.3390\/computers12100216"},{"key":"ref_14","unstructured":"Kaggle (2024, September 24). CIPLAB @ Yonsei University. Real and Fake Face Detection. Available online: https:\/\/www.kaggle.com\/datasets\/ciplab\/real-and-fake-face-detection."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.21512\/commit.v17i2.8761","article-title":"Classification of Deepfake Images Using a Novel Explanatory Hybrid Model","volume":"17","author":"Kerenalli","year":"2023","journal-title":"CommIT J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Coccomini, D.A., Caldelli, R., Falchi, F., Gennaro, C., and Amato, G. (2022, January 27\u201330). Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection. Proceedings of the 1st International Workshop on Multimedia AI against Disinformation (MAD \u201922), Newark, NJ, USA.","DOI":"10.1145\/3512732.3533582"},{"key":"ref_17","first-page":"339","article-title":"Data Augmentation-based Novel Deep Learning Method for Deepfaked Images Detection","volume":"20","author":"Farkhud","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_18","unstructured":"Kaggle (2024, September 24). Deepfake and Real Images. Available online: https:\/\/www.kaggle.com\/datasets\/manjilkarki\/deepfake-and-real-images."},{"key":"ref_19","unstructured":"Kaggle (2024, February 24). CelebFaces Attributes (CelebA) Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/jessicali9530\/celeba-dataset\/data."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Z., Han, Y., Hua, Z., Ruan, N., and Jia, W. (2021, January 20\u201325). Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00361"},{"key":"ref_21","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nie\u00dfner, M. (November, January 27). Faceforensics++: Learning to detect manipulated facial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e881","DOI":"10.7717\/peerj-cs.881","article-title":"Deep learning model for deep fake face recognition and detection","volume":"8","author":"Suganthi","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_24","unstructured":"Generated Photos (2024, September 27). 100K-Faces Dataset. Available online: https:\/\/generated.photos\/datasets."},{"key":"ref_25","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., and Jain, A.K. (2022, January 13\u201319). On the Detection of Digital Face Manipulation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA."},{"key":"ref_26","unstructured":"Yi, D., Lei, Z., Liao, S., and Li, S. (2014). Learning Face Representation from Scratch. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Soleimani, M., Nazari, A., and Moghaddam, M.E. (2023). Deepfake Detection of Occluded Images Using a Patch-based Approach. arXiv.","DOI":"10.2139\/ssrn.4115555"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analysing and improving the image quality of StyleGAN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., and Choo, J. (2018, January 18\u201323). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., and Tang, X. (2015, January 7\u201313). Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, P., Xu, M., and Wang, X. (2022). Detecting Compressed Deepfake Images Using Two-Branch Convolutional Networks with Similarity and Classifier. Symmetry, 14.","DOI":"10.3390\/sym14122691"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.32604\/iasc.2023.029653","article-title":"Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods","volume":"35","author":"Abir","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115","DOI":"10.31449\/inf.v47i7.4741","article-title":"Fake Image Detection Using Deep Learning","volume":"47","author":"Khudeyer","year":"2023","journal-title":"Informatica"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Doloriel, C.T., and Cheung, N.M. (2024). Frequency Masking for Universal Deepfake Detection. arXiv.","DOI":"10.1109\/ICASSP48485.2024.10446290"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Wang, O., Zhang, R., Owens, A., and Efros, A.A. (2020, January 13\u201319). CNN-generated images are surprisingly easy to spot... for now. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., and Verdoliva, L. (2021, January 5\u20139). Are GAN generated images easy to detect? A critical analysis of the state-of-the-art. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428429"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, Y., Nguyen, H.H., Lu, C.S., Zhang, Z., Sun, L., and Echizen, I. (2023). Generalised Deepfakes Detection with Reconstruct-ed-Blended Images and Multi-scale Feature Reconstruction Network. arXiv.","DOI":"10.1109\/IJCB62174.2024.10744491"},{"key":"ref_38","first-page":"1095","article-title":"Image Forgery Detection Using Deep Neural Network","volume":"10","author":"Nethravathi","year":"2023","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, X., Dong, C., Ji, J., Cao, J., and Li, X. (2021, January 11\u201317). Image manipulation detection by multi-view multi-scale supervision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01392"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"653","DOI":"10.12928\/telkomnika.v17i2.8976","article-title":"Image forgery detection using error level analysis and deep learning","volume":"17","author":"Sudiatmika","year":"2019","journal-title":"Telkomnika"},{"key":"ref_41","unstructured":"Kaggle (2024, May 03). CASIA 2.0 Image Tampering Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/divg07\/casia-20-image-tampering-detection-dataset\/code."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., Yang, S., Hu, S., Fang, Z., Fu, Y., Wu, X., and Wang, X. (2024). Masked conditional diffusion model for enhancing deepfake detection. arXiv.","DOI":"10.1109\/IJCNN60899.2024.10651330"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arshed, M.A., Mumtaz, S., Ibrahim, M., Dewi, C., Tanveer, M., and Ahmed, S. (2024). Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model. Computers, 13.","DOI":"10.3390\/computers13010031"},{"key":"ref_44","unstructured":"Thispersondoesnotexist.com (2024, March 15). Thispersondoesnotexist. Available online: https:\/\/thispersondoesnotexist.com."},{"key":"ref_45","unstructured":"Kaggle (2024, March 15). Synthetic Faces High Quality (SFHQ) Part 4. Available online: https:\/\/www.kaggle.com\/datasets\/selfishgene\/synthetic-faces-high-quality-sfhq-part-4."},{"key":"ref_46","unstructured":"Wang, T., Huang, M., Cheng, H., Ma, B., and Wang, Y. (2023). Robust Identity Perceptual Watermark Against Deepfake Face Swapping. arXiv."},{"key":"ref_47","unstructured":"Github (2024, February 24). [CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis. Available online: https:\/\/github.com\/yinanhe\/ForgeryNet."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:30:39Z","timestamp":1760027439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["computers14020060"],"URL":"https:\/\/doi.org\/10.3390\/computers14020060","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]}}}