Efficient Deepfake Detection Using AI

Deepfake technology poses a significant threat to the integrity of multimedia content, raising urgent concerns for media authentication and trustworthiness. This paper addresses the challenges of detecting deep fake videos, focusing on the impact of different dataset characteristics and the effectiveness of cross-dataset testing. We explore the application of reinforcement learning (RL) techniques for deepfake detection, particularly focusing on cross-dataset training. Two Convolutional Neural Network (CNN) models are employed, and the RL agent is trained using ten augmentations as actions. The Proximal Policy Optimization (PPO) and Deep Q Network (DQN) algorithms are evaluated for learning the policy, showcasing the potential of RL in enhancing the robustness of deep fake detection models. Furthermore, the paper investigates the influence of dataset char-acteristics, such as size, diversity, and quality, on the performance of deepfake detection models. It analyzes the challenges posed by datasets with varying characteristics and explores strategies to improve model generalization and adaptability across different datasets. The findings underscore the importance of considering dataset characteristics in the design and evaluation of deepfake detection systems, highlighting the need for standardized datasets and evaluation protocols in the field of multimedia forensics. The results demonstrate the efficacy of the proposed methodologies, highlighting the importance of dataset characteristics and cross-dataset testing in improving the reliability of deepfake detection systems.