DEEPFAKE DETECTION USING A HYBRID RESNEXT AND LSTM ARCHITECTURE

Авторы

  • abbaz primbetov tatu
  • Sarvar Maxmudjanov
  • Axadjon Naimov

Ключевые слова:

Resnext, LSTM, Deepfake.

Аннотация

Deepfakes pose a serious threat to media authenticity and public trust. This paper proposes a hybrid deep learning model combining ResNeXt and LSTM to detect deepfakes by capturing both spatial and temporal inconsistencies. ResNeXt extracts detailed frame-level features, while LSTM models temporal dependencies across video frames. Evaluated on benchmark datasets such as DFDC and Celeb-DF, the model achieves high accuracy and robust performance. The results confirm that integrating spatial and temporal features significantly improves deepfake detection, offering a reliable approach for video-based forensic analysis.

Библиографические ссылки

References

Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. Computers in Human Behavior Reports, 16, 100538.

[Rossler, A., et al. (2019). FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV.

[Gansbeke, W. V., et al. (2020). Temporal Awareness in Deepfake Detection. IEEE Transactions on Biometrics, Behavior, and Identity Science.

Masi, I., et al. (2020). Two-Stream Network for Deepfake Detection. CVPR Workshops.

Xie, S., et al. (2017). Aggregated Residual Transformations for Deep Neural Networks. CVPR (ResNeXt paper).

Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies,Matthias Nießner, “FaceForensics++: Learning to Detect Manipulated Facial Images” in arXiv:1901.08971.

Deepfake detection challenge dataset : https://www.kaggle.com/c/deepfake-detection- challenge/data Accessed on 26 March, 2020

Yuezun Li , Xin Yang , Pu Sun , Honggang Qi and Siwei Lyu “Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics” in arXiv:1909.12962

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Опубликован

2025-06-03

Как цитировать

primbetov, abbaz, Maxmudjanov, S., & Naimov , A. (2025). DEEPFAKE DETECTION USING A HYBRID RESNEXT AND LSTM ARCHITECTURE. Потомки Аль-Фаргани, (2), 87–94. извлечено от https://al-fargoniy.uz/index.php/journal/article/view/835

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