Deepfake Detection and the Impact of Limited Computing Capabilities
This addresses the challenge of controlling misinformation and mass manipulation by improving deepfake detection for users with limited computing capabilities, but it appears incremental as it focuses on analyzing existing techniques under constraints.
This work tackled the problem of detecting deepfakes across various datasets under limited computing resources, analyzing the applicability of different deep learning techniques and exploring approaches to enhance their efficiency, but no concrete results or numbers were provided.
The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of forged videos. This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency.