DeepFake Shield: A Multi-Modal Generative AI Fraud Detection Pipeline with Explicit Uncertainty Handling

Abstract

Synthetic media is now cheap to make and hard to judge by eye. A cloned voice can approve a payment, a generated face can get past a casual identity check, and a fluent paragraph written by a language model can pass for a statement from a real person. Most detectors cover only one of these cases, and almost all return a confident verdict even for input far outside their training data. This paper presents DeepFake Shield, a Flask-based web application that screens images, text, audio and video through one interface. Each modality is served by a pre-trained deep learning classifier, and a threshold layer turns the raw model score into one of three outcomes: authentic, synthetic or inconclusive. For video, the visual and audio streams are analysed separately and any disagreement between them is reported as a conflict. The paper gives a detailed justification for the topic, describes the architecture and decision logic, and sets out an evaluation protocol based on precision, recall, F1, ROC-AUC and Brier score with thresholds tuned on validation data only. We report the engineering checks completed so far and state clearly that they are not accuracy measurements.

Introduction

DeepFake Shield is a multimodal AI-based fraud detection system designed to identify potentially manipulated or artificially generated digital content. With the growing misuse of generative AI for identity fraud, voice cloning, deepfake videos, fabricated evidence, and misinformation, traditional verification methods and single-purpose detection tools are becoming insufficient.

The proposed system addresses these challenges by integrating four types of content analysis—images, text, audio, and video—into a single web application. It uses open, pre-trained models to analyze uploaded content and provides users with results through an accessible interface. The application also includes user registration and login, a dashboard, analysis history, content-type and status filters, and CSV export to support record-keeping and review.

A key feature of DeepFake Shield is its three-way classification system, which categorizes content as likely authentic, likely manipulated or AI-generated, or inconclusive. Instead of forcing a decision when the model is uncertain, the system can refer doubtful cases for human review. For video analysis, frames and audio are examined independently, and disagreements between their results are flagged for further investigation. This approach aims to reduce overconfidence and address the risks of false-positive and false-negative predictions.

The system is motivated by limitations in existing detection tools, including fragmented workflows, poor generalization to unfamiliar manipulation techniques, limited accessibility for ordinary users, and unreliable confidence scores. By using open pre-trained models and supporting local inference, DeepFake Shield also aims to improve reproducibility, reduce computational costs, and protect sensitive media from unnecessary sharing with third-party services.

The project is intended for journalists, fact-checkers, content moderators, businesses, researchers, and ordinary users who need to verify digital content. Its research focuses on integrating multiple detection models, evaluating the usefulness of an inconclusive category, and distinguishing basic software functionality tests from genuine evidence of detection accuracy.

Overall, DeepFake Shield aims to provide a unified, privacy-conscious, and user-friendly platform for preliminary deepfake and AI-generated content screening. However, its predictions should not be treated as definitive proof of authenticity or manipulation. Reliable deployment requires testing on diverse datasets, evaluating precision, recall and calibration, and conducting further human review, particularly for uncertain or high-risk cases.

Conclusion

DeepFake Shield shows that one accessible tool can screen images, text, audio and video, and that doing this honestly means admitting uncertainty. Returning to the research questions, four detectors can be placed behind one interface with their limits stated openly, a three-way decision rule gives a more honest output for ambiguous content, and a strict separation between testing and accuracy claims keeps the evaluation credible. The system is meant to support human reviewers and not to replace them. Future work will first complete the labelled evaluation and report the results with coverage and calibration. After that we plan API endpoints with batch processing, simple ensembles of detectors, adversarial evaluation, automatic threshold tuning on validation data and support for languages other than English. Longer-term directions include stream processing, a mobile client and federated learning, each of which raises its own privacy and robustness questions.

References

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Copyright

Copyright © 2026 Mrs. Reena Dewangan, Ms. Yogini Verma, Ms. Sanskruti Chaple, Ms. Lingam Sharon, Ms. Kritika Tandulkar, Mr. Om Margamwar, Mr. Vansh Bangre. 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.