Hibrit ResNeXt ve LSTM Mimarisi Kullanılarak Deepfake Video Algılama

Year 2025, EARLY VIEW, 1 - 1

Nurcan Yardımcı , Mohamed Ibrahim Abdi , Burhan Ergen

https://doi.org/10.2339/politeknik.1721371

Abstract

Deepfake içeriklerin artan yaygınlığı, bireysel gizlilik, medya güvenilirliği ve kamu güveni için ciddi bir tehdit oluşturmaktadır. Mevcut tespit yöntemleri genellikle çeşitli manipülasyon teknikleri ve video kalite seviyeleri arasında genelleme yapmakta zorlanmaktadır. Bu çalışma, ResNeXt-50'nin mekansal özellik çıkarma güçlerini LSTM ağlarının zamansal dizi modelleme yetenekleriyle birlikte kullanan derin öğrenmeye dayalı bir hibrit mimari sunmaktadır. Önerilen çerçeve, başlangıçta önceden eğitilmiş bir ResNeXt-50 omurgası aracılığıyla kare bazlı özellikler elde ederek ve ardından bir LSTM katmanı aracılığıyla zamansal dinamikleri inceleyerek video girişini işler. Deneysel değerlendirmeler, DFDC, Celeb-DF, FaceForensics++ ve DFD dahil olmak üzere kıyaslama veri kümeleri kullanılarak yürütülmüştür. Bulgular, geliştirilen modelin geleneksel CNN-LSTM kombinasyonlarından önemli ölçüde daha iyi performans gösterdiğini, DFDC veri kümesinde %95,7 ve diğer veri kümelerinde %90'ın üzerinde doğruluk elde ettiğini göstermektedir. Bu araştırma, hibrit derin öğrenme tekniklerinin gerçek dünya video kimlik doğrulama sistemlerinde pratik uygulanabilirliğini vurgulamakta ve sentetik ortam tespitinin büyüyen alanına yüksek performanslı bir çözüm sunmaktadır.

Keywords

DeepFake , ResNeXt-50 , LSTM , Sahte Video Tespiti , Hibrit Derin Öğrenme

Deepfake Video Detection Using a Hybrid ResNeXt and LSTM Architecture

Abstract

The growing spread of deepfake materials presents a serious threat to individual privacy, media credibility, and public trust. Existing detection methods often struggle to generalize across various manipulation techniques and video quality levels. This study proposes a hybrid A hybrid architecture based on deep learning is introduced, which leverages the spatial feature extraction strengths of ResNeXt-50 along with the temporal sequence modeling capabilities of LSTM networks. The suggested framework handles video input by initially obtaining frame-wise features via a pretrained ResNeXt-50 backbone and then examining temporal dynamics through an LSTM layer. Experimental evaluations were conducted using benchmark datasets, including DFDC, Celeb-DF, FaceForensics++, and DFD. Findings indicate that the developed model significantly outperforms conventional CNN-LSTM combinations, attaining 95.7% accuracy on the DFDC dataset and above 90% on the other datasets. This research highlights the practical applicability of hybrid deep learning techniques in real-world video authentication systems and contributes a high-performance solution to the growing field of synthetic media detection.

Keywords

DeepFake , ResNeXt-50 , LSTM , Fake Video Detection , Hybrid Deep Learning

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