Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection: Advancing Accuracy, Scalability, and Multimodal Integration

Summary

This paper proposes a hybrid Convolutional Neural Network-Generative Adversarial Network (CNN-GAN) framework for deepfake detection, integrating adversarial training, temporal analysis, and multimodal methods. The approach achieves 98.5% detection accuracy, demonstrates robust generalization across diverse datasets, and supports real-time optimization to address scalability, adversarial robustness, and misinformation mitigation.

Abstract

Deepfakes pose critical threats to digital media integrity and societal trust. This paper presents a hybrid deepfake detection framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address challenges in scalability, generalizability, and adversarial robustness. The framework integrates adversarial training, a temporal decay analysis model, and multimodal detection across audio, video, and text domains. Evaluated on the FaceForensics++ benchmark dataset, the hybrid CNN-GAN model achieved state-of-the-art performance with 98.5% accuracy, 99.1% precision, 97.9% recall, and a Receiver Operating Characteristic-Area Under Curve of 0.995, surpassing conventional models like ResNet50, XceptionNet, and EfficientNet. The temporal analysis component models the decay of deepfake detectability over time, guiding proactive retraining, while the multimodal module identified 97.8% of cross-modal inconsistencies. Simulation of deepfake propagation in social networks revealed significant socio-technical impacts, emphasizing the need for early detection systems and public awareness campaigns to curb misinformation spread. Optimized for real-time deployment, the framework balances high scalability with ethical considerations. This study highlights the importance of interdisciplinary approaches to deepfake detection in advancing AI-driven safeguards for digital ecosystems. Future work will focus on developing lightweight detection models, improving adversarial defenses, and expanding domain-specific applications to enhance the framework's applicability and resilience.

Introduction

Advancements in artificial intelligence (AI) and deep learning in recent years have transformed various domains, enabling applications such as natural language processing, computer vision, and autonomous systems¹. One of the most significant breakthroughs has been in generative modeling, particularly with Generative Adversarial Networks (GANs)². These models have enabled the creation of hyper-realistic synthetic media known as deepfakes, which involve the manipulation of audio, video, or images to convincingly mimic real-world subjects. While this technology offers creative possibilities in areas like filmmaking, virtual....

Protocol

This study did not involve human subjects or animal experiments. All deepfake data used were obtained from publicly available datasets, and no personally identifiable information was utilized. Consequently, formal ethical approval was not required. The research was conducted in accordance with institutional guidelines and ethical standards for studies involving artificial intelligence and data analysis. One of the primary challenges is ensuring that detection algorithms scale effectively to handle diverse datasets and real-world scenarios. Most existing methods are optimized for specific benchmarks, such as FaceForensics++ ⁷, but perform poorly....

Results

The proposed hybrid detection framework was rigorously evaluated across multiple dimensions, including detection accuracy and precision, scalability and generalization tests, adversarial robustness, and socio-technical impact analysis. The following sections summarize the key experimental findings.

Performance of the hybrid CNN-GAN model

We benchmarked our hybrid model against three state-of-the-art deepfake detecti.......

Discussion

The experimental results demonstrate that the proposed hybrid CNN-GAN framework, augmented with temporal analysis and multimodal integration, effectively addresses several core challenges in deepfake detection. In this discussion, we examine the implications of these findings, the strengths and limitations of our approach, and future directions for research and deployment.

Performance and contributions

The framework's superior performance on al.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Google API Client	Google	N/A	Google API Client is a Python client library for Google's discovery-based APIs. https://github.com/googleapis/ google-api-python-client
Intel	OpenCV	N/A	OpenCV is a library for computer vision. https://opencv.org
Python	Python Software Foundation	N/A	Python is a programming language. https://www.python.org
PyTorch	Facebook AI Research	N/A	PyTorch is a machine learning framework. https://pytorch.org