Multimodal Deepfake Detection Using Deep Learning

Abstract: Deepfake technology has evolved rapidly, enabling the creation of highly realistic manipulated images, audio, and videos. While these advancements have applications in entertainment and media, they also pose significant risks such as misinformation, identity fraud, and security threats. This research focuses on multimodal deepfake detection using deep learning techniques by combining visual and audio features for improved accuracy. The proposed approach integrates Convolutional Neural Networks (CNNs) for image analysis and Natural Language Processing (NLP) and audio-based models for detecting inconsistencies across modalities. By leveraging multimodal data, the system enhances detection robustness compared to unimodal approaches. Experimental results demonstrate that combining visual and audio cues significantly improves detection performance and generalization across different types of deepfakes. This system can be applied in social media monitoring, digital forensics, and cybersecurity applications.

Keywords: Deepfake Detection, Multimodal Learning, Machine Learning, CNN, NLP, Audio-Visual Analysis, Artificial Intelligence