Deep Fake Detection using Adversarial Ensemble Techniques

The rise of deepfake technology has resulted in unprecedented challenges in the field of digital media verification, posing significant threats to personal security, misinformation, and trust in digital content. In response to this pressing issue, this study presents an advanced deepfake detection system using deep learning approach. Specifically, the proposed system integrates ResNeXt and Long Short-Term Memory (LSTM) architectures to accurately identify the manipulated content. By leveraging the spatial-temporal features captured by these combined architectures, the proposed system aims to enhance the detection rate of deepfakes across various media formats. The proposed approach offers a robust solution to counteract the evolving sophistication of deepfake technology, thereby enhancing the integrity and authenticity of digital media content.