The rapid advancement in processing power has empowered deep learning algorithms to produce remarkably convincing human-synthesized videos, commonly referred to as "deep fakes." This technological progression raises concerns about potential malicious applications, such as blackmail, manipulation through revenge porn, or the exploitation of political unrest using realistic face-swapping deepfakes. In response to these challenges, we propose a novel deep learning-based technique designed to reliably differentiate between genuine videos and those generated by artificial intelligence. Our approach introduces an innovative method for automatically detecting replacement and recreation deep fakes. Leveraging the capabilities of Artificial Intelligence (AI) to combat AI-driven threats, our system employs a two-step process. Initially, frame-level features are extracted using a Res-Net Convolutional Neural Network (CNN). Subsequently, these features serve as input for training a Deep Neural Network (DNN) based on a Recurrent Neural Network (RNN) architecture. To assess the effectiveness of our method, we conducted extensive evaluations on a substantial and diverse dataset. This dataset was meticulously curated by combining various sources, including Face-Forensic++, Deepfake Detection Challenge, and Celeb-DF, aiming to simulate real-time scenarios and enhance the model's performance on real-world data. Our results demonstrate the system's capability to discern alterations in videos, effectively distinguishing between deep fakes and authentic content. Furthermore, we showcase the simplicity and reliability of our approach, illustrating its competitive performance. This research contributes to the ongoing efforts in developing robust solutions for identifying and mitigating the risks associated with the proliferation of AI-generated deep fake content in various contexts.
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