Context-Aware Deepfake Detection for Political Speeches

In recent years, the proliferation of deepfakes—AI-generated video that mimic the likeness and voices of political figures—has posed a significant threat to public trust and democratic processes. Deepfakes can be used to spread misinformation, damage reputations, and mislead the public with startling realism, making it difficult for the human eye to detect manipulation. A recent study found that deepfake videos increased by 900% between 2019 and 2022, with over 85% targeting political and public figures. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. By integrating Convolutional Neural Networks (CNN) and Natural Language Processing (NLP) techniques, it analyses both the audio and visual components of political media to identify synthetic content. This system aims to promote fair elections, uphold the integrity of political speech, and ensure that the public has access to accurate, verified information. With a focus on high accuracy and real-time detection, our system is built to function in live scenarios, providing timely responses to emerging deepfake threats.