REVIEW PAPER ON DEEP FAKE DETECTION USING DEEP LEARNING
DOI:
https://doi.org/10.29121/granthaalayah.v13.i4.2025.6177Keywords:
Deepfake Images, Ai-Generated Faces, Deep Learning, Image Forensics, Cnn, Fake Content DetectionAbstract [English]
In the modern digital age, the emergence of artificial intelligence has made it possible to create hyper-realistic human faces that do not exist in reality. Such AI-generated images, popularly referred to as deepfakes, pose an increasing threat as they can fool the human eye and be used for nefarious activities.
The study centers on the identification of such forged images via deep learning, that is, CNNs. Unlike the majority of current research that mainly focuses on video-based deep fake detection, our method tackles the problem at image level. This is important, as even our one single doctored image can be used to spread false information, impersonate others, or breach security mechanisms.
We built a detection model able to pick out subtle artifacts and inconsistencies-numerically, in many cases invisible to the naked eye-performed by the process of generating an image.
Our intention is not only to design and performative model but also help strengthen the general attempt at restoring and sustaining trust within digital material through developing available and precise fake image detection.
Downloads
References
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). Mesonet: A compact facial video forgery detection network. IEEE WIFS. https://doi.org/10.1109/WIFS.2018.8630761 DOI: https://doi.org/10.1109/WIFS.2018.8630761
Agarwal, N., & Farid, V. (2020). Detecting AI-generated images. arXiv preprint arXiv:2006.01567.
Chollet, F. (2017). Deep learning with Python. Manning Publications.
Flask Documentation. (2024). Flask Web Framework. Retrieved from https://flask.palletsprojects.com/
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization.
Li, Y., Chang, M., & Lyu, S. (2018). In ictu oculi: Exposing AI-generated fake face videos by detecting eye blinking. IEEE ICIP. https://doi.org/10.1109/WIFS.2018.8630787 DOI: https://doi.org/10.1109/WIFS.2018.8630787
Nguyen, H., Yamagishi, J., & Echizen, I. (2019). Capsule-forensics: Using capsule networks to detect forged images and videos. ICASSP. https://doi.org/10.1109/ICASSP.2019.8682602 DOI: https://doi.org/10.1109/ICASSP.2019.8682602
OpenAI. (2024). Chat-based AI APIs. Retrieved from https://openai.com/
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition.
TensorFlow Documentation. (2024). TensorFlow Core. Retrieved from https://www.tensorflow.org/
Wolf, T., Debut, L., Sanh, V., et al. (2020). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. https://doi.org/10.18653/v1/2020.emnlp-demos.6 DOI: https://doi.org/10.18653/v1/2020.emnlp-demos.6
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Shikha Singh, Shavez Khan, Chandan Pandey, Sandhya Sahani, Abhishek Singh, Vivek Patel
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
