Context-Aware Deepfake Detection for Political Speeches

Abstract

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.

Introduction

The paper provides a comprehensive study of deepfake technology and the development of an effective deepfake detection system using deep learning methods.

1. Introduction

• Deepfakes are AI-generated audio, video, or text content created using techniques like GANs and autoencoders.

• While useful in creative industries, they pose serious risks in politics, security, and media integrity due to their realism and potential misuse.

• Voice-based deepfakes are particularly difficult to detect and highly deceptive.

2. Literature Review

• Researchers have developed various models, including CNNs, RNNs, LSTMs, and capsule networks, to detect deepfakes.

• Techniques exploit visual artifacts (e.g., eye blinking, face warping, lighting issues) and physiological signals to distinguish fake from real.

• High detection accuracy (up to 99%) has been achieved in controlled settings, but real-time generalization remains a challenge.

3. Methodology

• The team used the Deepfake Detection Challenge (DFDC) dataset with 1,000 real and 1,000 fake videos.

• A CNN-based model was used, supplemented by preprocessing steps like resizing, normalization, and data augmentation.

• Detection strategies focused on visual inconsistencies, motion analysis, and metadata inspection.

• Balanced datasets and feature extraction were essential for building a robust classifier.

4. Results and Discussion

• A baseline CNN achieved 97.5% accuracy, confirming its strength in detecting artifacts.

• A ResNeXt-LSTM hybrid model further improved performance by combining spatial and temporal features.

• Despite high performance, limitations include the lack of generalizability to new deepfake techniques and the computational intensity of the models.

5. Future Scope

• Future work should focus on:

  • More diverse training datasets

  • Improved generalization to unknown deepfake types

  • Real-time detection using lightweight models

  • Adversarial robustness

  • Ethical and privacy concerns in deployment

6. Acknowledgment

• The authors thank their supervisor, the DFDC contributors, and the broader research community for their support.

Conclusion

This project illustrated a deep learning framework for recognizing deepfakes in video material with a highly accurate detection rate of 98% on the Deepfake Detection Challenge data set. Visual anomalies indicative of tampered media have been accurately detected by convolutional neural networks (CNNs). Our results further illustrate the importance of preprocessing data, i.e., face alignment and normalization, in further improving model reliability and accuracy. The findings of the study indicate the immense potential of deep learning methods to avert the emerging menace of deepfakes, despite issues with generalization, real-time capability, and adversarial robustness. With additional research and development, these models may significantly enhance public confidence and authenticity of digital media.

References

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Copyright

Copyright © 2025 Ayush Kumar Singh, Divyanshi Bansal, Harsh Bansal, Himanshu Vashishtha, Mr. Govind Kumar Rahul. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.