A MULTIMODAL DEEPFAKE DETECTION USING DEEP LEARNING

Deepfake technology has significantly advanced in recent years, enabling the creation of
highly realistic manipulated images, videos, and audio that can spread misinformation and threaten
digital trust. FauthAI is proposed as a multimodal deepfake detection framework designed to identify
such manipulated media effectively. The system analyzes multiple types of media simultaneously,
improving the reliability and accuracy of detection compared to traditional single-modality
approaches. The framework applies different deep learning models to process each media type.
Convolutional Neural Networks (CNNs) are used to detect spatial artifacts in images, while Recurrent
Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks capture temporal
inconsistencies in video sequences. For audio analysis, spectrogram-based models are utilized to
identify patterns associated with synthetic or manipulated audio signals. In addition to spatial and
temporal analysis, the system examines frequency-domain artifacts and extracts meaningful features
from each media modality. These extracted features help identify subtle inconsistencies that may not
be visible to the human eye, thereby improving the overall detection capability of the framework. To
enhance detection performance, FauthAI integrates the extracted features using multimodal fusion and
ensemble learning techniques. This approach allows the system to combine information from images,
videos, and audio, resulting in more accurate and robust identification of deepfake content .
Furthermore, the proposed framework is designed to be scalable and adaptable to emerging deepfake
generation techniques. Its flexible architecture enables continuous improvement and makes it suitable
for real-world media forensics, digital security, and misinformation detection applications.