Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection:...

Area of Science:

• Artificial Intelligence
• Computer Vision
• Cybersecurity

Background:

• Deepfakes present significant threats to digital media integrity and public trust.
• Existing deepfake detection methods face challenges in scalability, generalizability, and robustness against adversarial attacks.

Purpose of the Study:

• To develop and evaluate a hybrid deepfake detection framework integrating CNNs and GANs.
• To enhance the scalability, generalizability, and adversarial robustness of deepfake detection.
• To address the socio-technical impacts of deepfake propagation and misinformation.

Main Methods:

• Implemented a hybrid framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).
• Integrated adversarial training, temporal decay analysis, and multimodal detection (audio, video, text).
• Evaluated performance on the FaceForensics++ benchmark dataset.

Main Results:

• Achieved state-of-the-art performance with 98.5% accuracy, 99.1% precision, 97.9% recall, and an AUC of 0.995.
• Temporal analysis modeled detectability decay, guiding proactive retraining.
• Multimodal detection identified 97.8% of cross-modal inconsistencies.

Conclusions:

• The hybrid CNN-GAN framework offers a robust and scalable solution for deepfake detection.
• Interdisciplinary approaches are crucial for developing effective AI-driven safeguards against misinformation.
• Emphasizes the need for early detection systems and public awareness campaigns.