Unmasking the Illusion: An AI and ML Driven Approach to Face Swap Deepfake Detection

Abstract

Deepfake technology, particularly face-swap manipulation, has raised significant concerns regarding media authenticity andsecurity. This paper presents\"FaceSwapExposed,\" which is an innovative artificialintelligence and machine learning framework designed to detect face swap deepfakes with high accuracy. Our approachutilizesadual-branch convolutional neural network to analyze both high- and low-frequency facial features, enabling the robust identification of subtle artifacts introduced during face swaps. Comprehensive experiments on multiple benchmark datasets demonstrated that our method outperformed existing techniques, achieving a detection accuracy exceeding 95%. The model was trained using advanced data augmentation and regularization strategiestoensurereliabilityacrossvarious lighting conditions and resolutions. Our results underscore the potential of tailored deep learning models for mitigatingdeepfake proliferation. This research not only contributes to improved deepfake detection butalsoprovidesafoundationforfuturework in developing real-time and scalable authenticity verification systems. Our system exhibits promising capabilities in diverse scenarios.

Introduction

Overview:
Face swap deepfakes—where one person's face is superimposed onto another's body in video or images—pose serious risks to media authenticity, privacy, and public trust. Advances in deep learning have made these manipulations harder to detect, outpacing traditional detection methods.

Problem Statement:

• Traditional methods using hand-crafted features or basic artifact analysis often fail to detect subtle face-swap manipulations.

• Deepfakes evolve quickly, require real-time detection, and are vulnerable to adversarial attacks.

Proposed Solution – “Unmasking the Illusion”:

A specialized dual-branch Convolutional Neural Network (CNN) framework is developed to enhance detection accuracy by:

• Capturing high-frequency details (e.g., texture, edges)

• Capturing low-frequency patterns (e.g., color inconsistencies, blending artifacts)

• Fusing both branches for robust classification

Key Techniques:

• Uses FaceForensics++ and in-house datasets

• Applies data augmentation, regularization, and Bayesian hyperparameter tuning

• Trained and tested using PyTorch with GPU acceleration

• Evaluated with metrics including accuracy, F1-score, ROC-AUC, and confusion matrix

Challenges:

1. Limited Data Quality – Lack of diverse, high-quality deepfake samples

2. Evolving Deepfake Techniques – Constantly improving GANs hinder detection

3. High Computational Load – Requires significant GPU resources

4. Adversarial Attacks – Detection systems can be fooled with slight input changes

5. Poor Real-World Generalization – Performance drops in uncontrolled conditions

Future Scope:

• Larger, more diverse datasets to improve generalization

• Continual learning models that adapt to new deepfake methods

• Multi-modal systems that use visual, audio, and behavioral data

• Optimized inference using model pruning, quantization, or edge computing

• Cross-domain adaptability to detect different deepfake formats

• Explainable AI and privacy-preserving techniques like federated learning

• Collaboration with platforms and regulators to mitigate spread and standardize detection

Conclusion

In this research paper, we exploredanAIand ML-driven approach to detecting face-swap deepfakes, which are becoming increasingly prevalent in the digital world. Our findings highlight the needforcontinuousevolutionin detection techniquestokeepupwiththerapid advancements in deepfake technology. By employing deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we were able to significantly improve the accuracy of deepfake detection compared to traditional methods. Despite the promising results, several challenges remain. Theseincludetheneedfor high-quality, diverse training datasets, the computational cost of deploying real-time detection systems, and the vulnerability of detection models to adversarial manipulation. Moreover, the ability of deepfake technology to mimic real-world conditions with high fidelity makes generalization to real-world scenarios a persistent challenge. Therefore, while AI and ML offer great potential in deepfake detection, further research is necessary to develop morerobust, scalable, and efficient methods. Future work should focus on creating diverse datasets, developing algorithms that can detect subtle inconsistencies in face-swap deepfakes, and improving the computational efficiency of detection systems. While our approach has demonstrated strong performance in controlled environments, several challenges remain in terms of generalization, real-time detection, and resilience against adversarial attacks. The computationalcomplexityinvolvedin processing high-resolution videos and maintainingaccuracyinreal-worldconditions underscores the need for further optimization and innovation in model design. Moreover, the adversarial nature of deepfake creation means that detection systems

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Copyright

Copyright © 2025 Priya P, Soban Babu V, Gopinath V, Sofiya S, Dhivya V. 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.