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Trustworthy Deepfake Detection: Explainable LIME Method of ViT and CNN Architectures Cover

Trustworthy Deepfake Detection: Explainable LIME Method of ViT and CNN Architectures

Open Access
|Jun 2026

References

  1. Altuncu, E., V. N. L. Franqueira, S. Li. Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review. – arXiv Preprint arXiv:2208.10913, 2022.
  2. Mirsky, Y., W. Le e. The Creation and Detection of Deepfakes. – ACM Computing Surveys, Vol. 54, 2021, No 1, pp. 1-41. DOI: 10.1145/3425780.
  3. Perov, I., D. Gao, N. Chervoniy, K. Liu, S. Marangonda, C. Umé, Dpfks, C. S. Facenheim, R. P. Luis, J. Jiang, S. Zhang, P. Wu, B. Zhou, W. Zhang. DeepFaceLab: Integrated, Flexible, and Extensible Face-Swapping Framework. – arXiv Preprint arXiv:2005.05535, 2020.
  4. Kowalski, M. FaceSwap. – GitHub Repository, 2024. https://github.com/MarekKowalski/FaceSwap/
  5. Contributors, FaceFusion. – In: GitHub Repository, 2024. https://github.com/facefusion/facefusion
  6. Rombach, R., A. Blattmann, D. Lorenz, P. Esser, B. Ommer. High-Resolution Image Synthesis with Latent Diffusion Models. – In: Proc of IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR’22), New Orleans, USA, 2022, pp. 10684-10695.
  7. Li, Y., X. Yang, P. Sun, H. Qi, S. Lyu. Celeb-DF: A Large-Scale Challenging Dataset for Deepfake Forensics. – In: Proc of IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR’20), 2020, pp. 3207-3216.
  8. Rossler, A., D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Niessner. FaceForensics++: Learning to Detect Manipulated Facial Images. – In: Proc of IEEE/CVF Int. Conf. on Computer Vision (ICCV’19), 2019, pp. 1-11.
  9. Shi, L., J. Zhang, Z. Ji, J. Bai, S. Shan. Real Face Foundation Representation Learning for Generalized Deepfake Detection. – Pattern Recognition, Vol. 161, 2025, 111299. DOI: 10.1016/j.patcog.2024.111299.
  10. Liu, D., Z. Dang, C. Peng, Y. Zheng, S. Li, N. Wang, X. Gao. Fedforgery: Generalized Face Forgery Detection with Residual Federated Learning. – IEEE Transactions on Information Forensics and Security, Vol. 18, 2023, pp. 4272-4284. DOI: 10.1109/TIFS.2023.3293951.
  11. Kim, H. Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition. – Applied Sciences, Vol. 15, 2025, No 7, 3613. DOI: 10.3390/app15073613.
  12. Sharma, J., S. Sharma, V. Kumar, H. S. Hussein, H. Alshazly. Deepfakes Classification of Faces Using Convolutional Neural Networks. – Traitement du Signal, Vol. 39, 2022, No 3. DOI: 10.18280/ts.390330.
  13. Naeem, S., R. Al-Sharawi, M. R. Khan, U. Tariq, A. Dhall, H. Al-Nashash. Real, Fake, and Synthetic Faces – Does the Coin Have Three Sides? – In: Proc. of IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG), Istanbul, Turkey, 2024. DOI: 10.1109/FG59268.2024.10581973.
  14. Jheelan, J., S. Pudaruth. Using Deep Learning to Identify Deepfakes Created Using Generative Adversarial Networks. – Computers, Vol. 14, 2025, No 2. DOI: 10.3390/computers14020060.
  15. Yang, W., Y. Wei, H. Wei, Y. Chen, G. Huang, X. Li, R. Li, N. Yao, X. Wang, X. Gu, M. B. Amin, B. Kang. Survey on Explainable AI: From Approaches, Limitations, and Applications Aspects. – Human-Centric Intelligent Systems, Vol. 3, 2023, pp. 161-188. DOI: 10.1007/s44230-023-00038-y.
  16. Salih, A., Z. Raisi-Estabragh, I. B. Galazzo, P. Radeva, S. Petersen, G. Menegaz, K. Lekadir. A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME. – Advanced Intelligent Systems, 2023, arXiv:2305.02012.
  17. Mersha, M., K. Lam, J. Wood, A. Al Shami, J. Kalita. Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction. – Neurocomputing, Vol. 599, 2024, 128111. DOI: 10.1016/j. neucom.2024.128111.
  18. Tariq, S., S. S. Woo, P. Singh, I. Irmalasari, S. Gupta, D. Gupta. From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Users. – In: Proc of 33rd ACM Int. Conf. on Multimedia (MM’25), 2025, pp. 11716-11725. DOI: 10.1145/3746027.3755786.
  19. Mansoor, N., A. I. Iliev. Explainable AI for Deepfake Detection. – Applied Sciences, Vol. 15, 2025, No 2, Article 725. DOI: 10.3390/app15020725.
  20. Tsigos, K., E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris. Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection. – In: ACM, New York, USA, 2024, pp. 37-45. DOI: 10.1145/3643491.3660292.
  21. Ribeiro, M. T., S. Singh, C. Guestrin. Why Should I Trust You? Explaining the Predictions of Any Classifier. – In: Proc of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, USA, 2016, pp. 1135-1144. DOI: 10.1145/2939672.2939778.
  22. Schallner, L., J. Rabold, O. Scholz, U. Schmid. Effect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Data. – arXiv Preprint arXiv:1910.07856, 2019.
  23. Hase, P., M. Bansal. Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? – In: Proc of Annual Meeting of the Association for Computational Linguistics (ACL’20), 2020, pp. 5540-5552. DOI: 10.18653/v1/2020. acl-main.491.
  24. Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, N. Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. – In: Proc of Conf. on Learning Representations (ICLR’21), 2021.
  25. Simonyan, K., A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. – arXiv preprint arXiv:1409.1556, 2014.
  26. He, K., X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. – In: Proc of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’16), 2016, pp. 770-778.
  27. Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the Inception Architecture for Computer Vision. – In: Proc of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’16), 2016, pp. 2818-2826.
  28. Sandler, M., A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. – In: Proc of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’18), 2018, pp. 4510-4520.
  29. Deng, J., W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. – In: Proc of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’09), 2009, pp. 248-255.
  30. Gong, R., R. He, D. Zhang, A. K. Sangaiah, M. J. F. Alenazi. Robust Face Forgery Detection Integrating Local and Global Texture Information. – EURASIP Journal on Information Security, Vol. 2025, 2025, 3. DOI: 10.1186/s13635-025-00189-4.
  31. Xiao, Y., Y. Zhou, P. Cheng, L. Ni, X. Wu, T. Zheng. An Attention-Based Framework for Detecting Face Forgeries: Integrating Efficient-ViT and Wavelet Transform. – Mathematics, Vol. 13, 2025, No 16, Article 2576. DOI: 10.3390/math13162576.
  32. Man, Q., Y.-I. Cho. Exposing Face Manipulation Based on GAN-Transformer and Fake Frequency Noise Traces. – Sensors, Vol. 25, 2025, No 5, 1435. DOI: 10.3390/s25051435.
  33. Abirami, P. Enhanced Fake Image Detection in Social Media Using Vision Transformer. – Int. J. for Research in Applied Science and Engineering Technology, Vol. 13, 2025, pp. 4570-4574. DOI: 10.22214/ijraset.2025.69302.
  34. Cirillo, L., A. Gervasio, I. Amerini. Explainability-Driven Adversarial Robustness Assessment for Generalized Deepfake Detectors. – EURASIP Journal on Information Security, Vol. 2025, 2025, 23. DOI: 10.1186/s13635-025-00211-9.
  35. Xhlulu. 140k Real and Fake Faces Dataset. – Kaggle Dataset, 2020.
  36. NVIDIA Research. Flickr-Faces-HQ (FFHQ) Dataset. – GitHub Repository, 2019.
  37. Karras, T., S. Laine, T. Aila. A Style-Based Generator Architecture for Generative Adversarial Networks. – In: Proc of IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR’19), 2019. DOI: 10.1109/CVPR.2019.00453.
  38. Tunguz, B. 1 Million Fake Faces Generated by StyleGAN. – In: Kaggle Dataset, 2020.
  39. Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin. Attention Is All You Need. – In: Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.
DOI: https://doi.org/10.2478/cait-2026-0014 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 61 - 78
Submitted on: Dec 16, 2025
Accepted on: Mar 5, 2026
Published on: Jun 13, 2026
In partnership with: Paradigm Publishing Services

© 2026 Zoulikha Koudad, Amina Bekkouche, Hamed Benahmed, Mourad Hadjila, Mohammed Merzoug, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.