Towards a better classification of deepfake videos - Vieira, Matheus P. R.; C., Arthur Negrão de F. M.; Silva, Guilherme A. L.; Luz, Eduardo; Silva, Pedro (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
The increasing accessibility and realism of deepfake technology pose serious threats to information integrity, privacy, and public trust. In this work, we propose a deep learning model for detecting deepfake videos based on short video segments. The architecture combines a TimeDistributed wrapper over an Xception backbone with global average pooling and a dense classification layer. To enhance performance, a Bayesian hyperparameter optimization strategy was employed, tuning both architectural and training parameters. The proposed model was evaluated on the Celeb-DF-V2 dataset, using only 24 frames per video, approximately one second of content. Despite this constraint, the model achieved a competitive accuracy of 97.30% and an AUC of 0.9957, outperforming several existing approaches that rely on longer video sequences. These results demonstrate the feasibility of detecting deepfakes efficiently using shorter clips, suggesting a viable direction for real-time and resource-constrained applications.