AI-Powered Detection of Cyber Attacks: Addressing Deepfakes and Identity Theft

Abstract

The proliferation of deepfake technologies has introduced significant challenges to cybersecurity, facilitating sophisticated identity fraud and misinformation dissemination. This study presents a comprehensive AI-driven detection framework that integrates convolutional neural networks (CNNs), ensemble classifiers, and behavioral analysis for the identification of manipulated multimedia content and identity theft. Utilizing datasets such as DFDC, FaceForensics++, and a custom identity fraud dataset, the system employs Preprocessing techniques including normalization, augmentation, and Error Level Analysis (ELA). Experimental results demonstrate 97% accuracy for visual deepfake detection, 98.5% for audio stream analysis, and 91.7% for identity fraud detection using Capsule Networks. These findings underscore the potential of the proposed architecture in real-time cyber threat mitigation and offer a foundation for future AI-based forensic systems.

Introduction

The widespread accessibility of AI tools—particularly Generative Adversarial Networks (GANs)—has led to the proliferation of deepfakes: hyper-realistic synthetic media that threaten political integrity, corporate security, and personal privacy. Traditional detection methods like digital watermarking and metadata analysis are increasingly ineffective. At the same time, identity theft is evolving via AI-enhanced spoofing and social engineering.

This research proposes a unified, AI-driven detection system that integrates visual, audio, and behavioral analysis to detect both deepfakes and identity fraud in real time. The system combines CNNs, Capsule Networks, LSTM, and ensemble learning techniques to improve detection accuracy, speed, and adaptability.

II. Literature Review

• CNN-LSTM architectures help detect temporal inconsistencies in video deepfakes with 92% accuracy.

• Capsule Networks enhance spatial anomaly detection, outperforming CNNs.

• Behavioral biometrics have shown potential in detecting identity theft but often produce high false positives (~18%).

• Ensemble models combining multiple classifiers (e.g., CNN + SVM/KNN) offer better generalization and robustness.

III. Methodology

A. System Architecture

• Inputs: Video, image, audio, and document files

• Preprocessing: YOLO-based frame extraction, normalization, Error Level Analysis (ELA), contrast enhancement

• Feature Extraction: CNNs identify visual and audio anomalies; behavioral patterns are analyzed

• Classification: Ensemble of SVM, KNN, and Capsule Networks to flag manipulated or fraudulent data

• Output: Real-time alerts and verification results

B. Datasets Used

• DFDC (Deepfake Detection Challenge): 100,000+ labeled video samples

• FaceForensics++: High-resolution facial manipulation data

• Custom Dataset: Forged credentials, phishing documents, biometric anomalies

C. Models and Tools

• CNNs: AlexNet, ShuffleNet

• Audio: Random Forest on spectral features

• Classifiers: SVM-KNN ensemble

• Capsule Networks: For structural validation

IV. Results

• CapsuleNet + Ensemble had the best performance across both detection types, with high accuracy and fast inference.

• ROC curves showed superior AUC scores for CapsuleNet and CNN+LSTM models, confirming their reliability.

• Larger datasets (>20,000 samples) improved performance significantly, though returns plateaued beyond a certain size.

V. Discussion

• High-quality, diverse datasets are essential for model accuracy.

• Ensemble models outperform single classifiers.

• Computational efficiency and scalability make the system suitable for real-time, multi-modal threat detection.

VI. Challenges and Future Work

1. Dataset Dependence: Deepfakes evolve rapidly, requiring continuous data updates.

2. Computational Demands: Capsule Networks are resource-intensive, limiting real-time use in low-power environments.

3. Adversarial Attacks: Attackers may create deepfakes specifically designed to bypass detectors.

4. Cross-Modal Fusion: Synchronization between audio and video cues needs refinement.

5. Privacy and Deployment:

   • Federated learning is proposed to protect user data.

   • Lightweight models are needed for mobile and IoT deployment.

   • Explainable AI (XAI) is important for transparency in forensic/legal use cases.

Conclusion

This study introduces a comprehensive AI-driven framework for the detection of deepfakes and identity fraud using multimodal inputs and advanced deep learning techniques. The integration of CNN-based visual processing, spectral audio forensics, and behavioral anomaly detection achieves high accuracy with low latency. Capsule Networks further enhance structural anomaly detection in identity documents. Experimental results affirm the system’s viability for deployment in security-critical environments. Future research will focus on improving generalizability through federated learning, reducing model bias, and deploying lightweight variants for edge devices. This study demonstrates a hybrid AI framework for multi-modal cyber threat detection, achieving state-of-the-art accuracy (94.3%) and real-time performance. Future directions involve federated learning for privacy preservation and edge-computing optimization. The proposed system achieved high accuracy across multiple benchmarks, demonstrating its effectiveness in identifying manipulated visual and audio content as well as fraudulent identity behaviours. The combination of CNN architectures with classifiers like Random Forest, SVM, and KNN, along with innovative Preprocessing methods such as Error Level Analysis, contributed to the system’s strong performance.

References

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Copyright

Copyright © 2025 Ashwin Dumane, Ritesh Mohod, Khushi Chafale, Pooja Shirbhate, Prof. P. P. Shelke. 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.