DEEPFAKE DETECTION USING A HYBRID RESNEXT AND LSTM ARCHITECTURE

primbetov, abbaz, Maxmudjanov, Sarvar, Naimov , Axadjon

Al-Farg'oniy avlodlari · 2025-yil

Annotatsiya

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.

Maqola ma’lumotlari
Mualliflarprimbetov, abbaz, Maxmudjanov, Sarvar, Naimov , Axadjon
JurnalAl-Farg'oniy avlodlari
Nashr sanasi2025-06-03
Son2
Betlar87-94
TilRus

Kalit so‘zlar

Resnext, LSTM, Deepfake.

Ilmiy soha

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