Synthetic Media Analysis Using Deep Learning

Abstract

In an era characterized by the rapid evolution of digital content creation, synthetic media, particularly deepfake videos, present a formidable challenge to the veracity and integrity of online information. Addressing this challenge requires sophisticated analytical techniques capable of discerning between authentic and manipulated media. This research paper presents a comprehensive study on synthetic media analysis leveraging deep learning methodologies. The suggested method integrates advanced deep learning models, including Convolutional Neural Networks (CNNs) like VGG (Visual Geometry Group), alongside recurrent structures such as LSTM (Long Short-Term Memory). These models are trained and evaluated on a meticulously curated dataset, ensuring diversity and relevance in the synthetic media samples. To facilitate experimentation and reproducibility, the dataset is securely hosted on a reliable platform such as Google Drive. Prior to model training, preprocessing steps including frame extraction are employed to extract essential visual features from the video data. The VGG model serves as a feature extractor, capturing high-level representations of visual content, while the LSTM model learns temporal dependencies and contextual information across frames. Following comprehensive experimentation, the proposed method's ability to detect synthetic media is thoroughly assessed, utilizing metrics such as accuracy. This research contributes to the ongoing discourse on digital media forensics by providing insights into the efficacy of deep learning techniques for synthetic media analysis. The findings underscore the importance of continuous research and development in combating the proliferation of synthetic media, thereby safeguarding the authenticity and trustworthiness of online content. Index Terms—CNN, LSTM, VGG