Cybersecurity
Network Intrusion Detection System
Machine-learning anomaly detection over live network traffic
- Python
- Scapy
- XGBoost
- Kafka
- +2
Cybersecurity · Capstone
Frame CNN plus a temporal model that flags manipulated faces
Overview
Detects face-swap and reenactment videos by cropping faces from each frame with a CNN feature extractor, then reading the sequence with a temporal model that catches the flicker and blending artefacts a still image hides. It is trained on the public FaceForensics++ dataset, and the report presents ROC and per-manipulation metrics rather than a headline accuracy figure.
What makes it stand up
Module breakdown
Detects, crops and aligns faces from sampled frames.
CNN that scores each face crop for manipulation cues.
Sequence model that reads inconsistency across frames.
Reports AUC and metrics split by manipulation method.
Uploads a clip and shows a verdict with a saliency heatmap.
After this, you will be able to
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Cybersecurity
Machine-learning anomaly detection over live network traffic