ForgeryDetect: AI-driven Deep Fake Identification

The rise of deepfake videos presents substantial obstacles in guaranteeing the genuineness and reliability of visual media material. This study focuses on the crucial problem of identifying deepfake content by introducing a sophisticated method that combines ResNext CNNs along with LSTM models, utilizing transfer learning approaches. Employed a comprehensive approach by using a wide array of datasets such as FaceForensics++, Celeb-DF, and the Deepfake Detection Challenge. Utilizing transfer learning, a ResNext CNN that has been pre-trained is employed to extract complex features from video frames. This enables the subsequent training of an LSTM layer to identify temporal patterns and distinctive cues that are exclusive to modified videos. The effectiveness of fusion model has been demonstrated through extensive experimentation and validation on these rich datasets. It demonstrates a strong ability to reliably distinguish between authentic and manipulated movies, regardless of the different facial alteration techniques, environmental variables, and complex situations seen in real-life situations. The integration of ResNext-based feature extraction with LSTM-based temporal modeling shows substantial progress in the detection of deepfake videos. The efficacy of approach is highly promising, since it provides both dependability and scalability. This study makes a significant contribution to the field of deepfake identification by offering a powerful collection of techniques to tackle the growing concerns surrounding disinformation and manipulation in visual media.