Deep Fake Using Audio and Image Detection

Abstract

In the AI-driven era, deep fakes, generated through advanced techniques like Generative Adversarial Networks (GANs), present significant threats by creating highly realistic yet fabricated media. While audio deep fakes have received considerable attention, the detection of manipulated images remains underexplored, creating a critical gap in comprehensive deep fake identification. Our proposed system bridges this gap by integrating transfer learning for enhanced detection across both fake audio and manipulated images. For image analysis, we utilize the VGG19 architecture, leveraging its deep convolutional layers and pre-trained weights to effectively identify visual artifacts and manipulations. For audio detection, we employ a hybrid model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), where the CNN extracts spatial features from audio spectrograms, while the RNN captures temporal dependencies to ensure robust analysis of audio authenticity. This hybrid approach allows for a comprehensive, dual-modal detection system that addresses both visual and auditory deep fakes. By combining these methodologies, the system ensures no manipulation indicators are overlooked, providing enhanced reliability and security in detecting digital content tampering. Ultimately, our system contributes to safeguarding the integrity of information, offering a powerful tool to combat the evolving threat of deep fakes in the digital landscape.

Introduction

Overview

The advancement of deep learning, especially Convolutional Neural Networks (CNNs), has led to the creation of highly realistic fake audio and images—commonly known as deepfakes. These can convincingly imitate real people or events, raising ethical, legal, and social concerns, especially when used for misinformation or manipulation.

Challenges and Implications

• Deepfakes can influence public opinion, politics, and digital trust.

• There's a growing need for detection systems and regulatory frameworks to counter their misuse.

Literature Survey Highlights

1. ResNext + LSTM models enhance video deepfake detection by analyzing spatial and temporal features.

2. Comprehensive reviews show AI methods (e.g., CNNs, RNNs) are critical for both detection and creation of deepfakes.

3. Social media use and conspiracy beliefs affect people's ability to detect deepfakes.

4. Audio classifiers are vulnerable to adversarial attacks, prompting the need for stronger models.

5. Realistic deepfakes can increase believability, while implausible ones may undermine trust in public figures.

Proposed System

• Hybrid RNN-based model for detecting manipulations in both audio and image data.

• A web-based interface allows users to upload content for real/fake verification.

• Training and testing datasets are split 70/30 using Kaggle-sourced fake audio and image datasets.

System Architecture

• Audio Preprocessing: Uses MFCCs to extract key sound features.

• Image Preprocessing: Includes normalization, resizing, and augmentation to prepare images.

• Feature Extraction: RNNs are used to detect patterns and temporal dependencies in both modalities.

• Model Training: RNN learns to classify content as "Real" or "Fake" using labeled data.

Performance Metrics

• Accuracy: Measures overall correct predictions.

• Precision: Indicates reliability of positive predictions.

• Recall: Measures the model’s ability to detect all real positives.

• F1 Score: Balances precision and recall, especially useful for imbalanced datasets.

Conclusion

The proposed system leverages a hybrid approach with Recurrent Neural Networks (RNN) to detect manipulations in both audio and image content, effectively addressing the unique challenges posed by multimedia forensics. By separately training on audio and image datasets, the system enhances detection accuracy and reliability for each media type. The integration of RNNs allows for better modeling of temporal dynamics in audio data and advanced feature extraction in images, making it a powerful tool for identifying fake or manipulated content. Additionally, the user-friendly web application simplifies the process for users, allowing them to upload and analyze content seamlessly. This system represents a significant advancement in multimedia forensics, expanding detection capabilities while providing a foundation for future research in combating digital manipulations. To further improve the system, future iterations could incorporate additional machine learning models, such as Convolutional Neural Networks (CNNs), to complement the RNN in image analysis. Moreover, expanding the dataset by incorporating more diverse types of manipulations could help increase robustness.

References

[1] Z. A. Baig et al., “Future challenges for smart cities: Cyber-security and digital forensics,” Digit. Investig., vol. 22, pp. 3–13, Sep. 2017. [2] H. Zimmerman, “The data of you: Regulating private industry’s collection of biometric information,” U. Kan. L. Rev., vol. 66, p. 637, 2017. [3] A. K. Jain and A. Kumar, “Biometric recognition: an overview,” in [4] Second generation biometrics: The ethical, legal and social context, Springer, 2012, pp. 49–79. [5] D. Lillis, B. A. Becker, T. O. Sullivan, and M. Scanlon, “Current Challenges and Future Research Areas for Digital Forensic Investigation INVESTIGATION,” no. c, 2016. [6] D. Ramos-Castro, J. Gonzalez-Rodriguez, and J. Ortega-Garcia, “Likelihood ratio calibration in a transparent and testable forensic speaker recognition framework,” in 2006 IEEE Odyssey-The Speaker and Language Recognition Workshop, 2006, pp. 1–8. [7] A. Saleema and S. M. Thampi, “Voice Biometrics: The Promising [8] Future of Authentication in the Internet of Things,” in Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, IGI Global, 2018, pp. 360–389. [9] B. Zawali, R. A. Ikuesan, V. R. Kebande, S. Furnell, and A. A- [10] Dhaqm, “Realising a Push Button Modality for Video-Based [11] Forensics,” Infrastructures, vol. 6, no. 4, p. 54, 2021. [12] Chunlei Peng, Huiqing Guo, Decheng Liu, Nannan Wang, Ruimin Hu, Xinbo Gao. (2023) Deep Fidelity: Perceptual Forgery Fidelity [13] Assessment for Deepfake Detection arXiv:2312.04961v1 [14] Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, Wei Xia. (2021) Learning Self-Consistency for Deepfake Detection IEEE/CVF International Conference on Computer Vision (ICCV) [15] Bojia Zi ,Minghao Chang ,Jingjing Chen, Xingjun Ma, Yu-Gang Jiang. (2021) Wild Deepfake: A Challenging Real- World Dataset for Deepfake Detection arXiv:2101.01456v1 [16] Kaede Shiohara Toshihiko Yamasaki. (2022) Detecting Deep fakes with Self-Blended Images IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [17] Shichao Dong, Jin Wang, Jiajun Liang, Haoqiang Fan, and Renhe Ji. (2022) Explaining Deepfake Detection by Analysing Image Matching. arXiv:2207.09679v1 [18] Anubhav Jain, Nasir Memon, Julian Togelius. (2022) A Dataless FaceSwap Detection Approach Using Synthetic Images. IEEE International Joint Conference on Biometrics, IJCB. Institute of Electrical and Electronics Engineers Inc. [19] Fatima Maher Salman and Samy S. Abu-Naser. (2022) Classification of Real and Fake Human Faces Using Deep Learning. [20] International Journal of Academic Engineering Research (IJAER) 6 (3). [21] Tiewen Chen , Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang. (2024) Masked Conditional Diffusion Model for Enhancing Deepfake Detection. ArXiv, abs/2402.0054.

Copyright

Copyright © 2025 Dr. S. Udayashree, Dr. R. G. Suresh Kumar, Ms. S. Tharunika, Ms. P. Ramadevi, Ms. D. Latika, Ms. S. Pavithra. 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.