SentinelAI: A Deep Learning-Based Real-Time Image Forgery Detection System

SentinelAI is a real-time image forgery detection system based on EfficientNet and MTCNN, designed to detect the authenticity of face images in camera real-time video streams, providing technical solutions to prevent Deepfake and other image manipulation technologies.

• Author/Maintainer: Yogavelan M D (junior engineering student)
• Source: GitHub
• Original Title: SentinelAI
• Link: https://github.com/Yogavelan2007/SentinelAI
• Release Time: May 26, 2026

With the rapid development of generative AI technology, image forgery and manipulation techniques have become increasingly prevalent and difficult to identify. Deepfake technology can generate realistic fake face images and videos, leading to severe trust crises in society—such as misinformation spread, identity fraud, political manipulation, and financial scams. SentinelAI is developed as a technical solution to this problem, providing real-time visual feedback for users to detect face authenticity in live video streams.

SentinelAI's architecture focuses on lightweight and efficiency, including three core components:

1. EfficientNet: A high-efficiency CNN by Google, using compound scaling to adjust depth, width, and resolution uniformly, balancing accuracy and computational cost. It is used to distinguish feature patterns between real and fake faces.
2. MTCNN: A cascaded CNN (P-Net → R-Net → O-Net) for real-time face detection, progressively refining face positions and key points.
3. Real-Time Workflow:
   • Video capture via OpenCV
   • Face detection with MTCNN
   • Image classification using EfficientNet
   • Result visualization (green border for real faces, red for fake, plus confidence percentage)

Development Environment:

• Python 3.12
• Dependencies: PyTorch (deep learning framework), timm (pre-trained EfficientNet models), OpenCV (video capture/image processing), facenet-pytorch (MTCNN implementation). Visualization Design:
• Green border: Real face (REAL)
• Red border: Fake face (FAKE)
• Confidence percentage: Shows model's confidence in the result.

SentinelAI's real-time capability makes it suitable for multiple scenarios:

1. Video Conference Security: As a plugin to detect tampered video streams and prevent Deepfake identity impersonation.
2. Identity Verification Enhancement: An extra security layer in remote identity verification (finance, government) to improve safety.
3. Content Moderation: Assist social media platforms in marking suspicious forged content, reducing manual workload.
4. Education & Training: A practical project for students learning deep learning and computer vision, demonstrating real-world application of advanced models.

As a student project, SentinelAI has room for improvement:

1. Model Generalization: Current model may perform poorly on new forgery techniques or low-quality images; need diverse training data and adversarial training.
2. Multi-Frame Analysis: Single-frame detection can be enhanced with time-continuous multi-frame analysis for better accuracy.
3. Performance Optimization: Use model quantization/pruning to run smoothly on resource-limited devices.
4. Adversarial Attack Protection: Strengthen model robustness against adversarial samples.

• FaceForensics++: A widely used Deepfake detection benchmark dataset with various fake video samples.
• DeepfakeDetection: Meta's challenge project promotes technological development.
• SentinelAI's Advantages: Simplicity, real-time performance, clear code structure (easy to understand and extend) as a student project.

SentinelAI, though small-scale, reflects key trends and educational value:

• Tech Integration: Combines EfficientNet and MTCNN into a complete system, showing practical engineering ability.
• Problem-Oriented: Addresses real-world Deepfake issues with appropriate tech selection.
• Open-Source Value: Serves as a reference for learners, promoting tech popularization. For learners in computer vision/deep learning, it's an excellent entry project covering the full development process (requirements analysis → tech selection → model integration → visualization). As forgery techniques evolve, continuous updates are needed; open-source projects like SentinelAI provide a foundation for community innovation to build a safer digital environment.