VERIFAL Comprehensive Fake Detection using Deep Learning скачать в хорошем качестве

VERIFAL Comprehensive Fake Detection using Deep Learning

VERIFAL is a comprehensive fake detection framework powered by Deep Learning, designed to identify manipulated or synthetic content such as deepfakes, edited images, tampered videos, and AI-generated media. In this video, we explain the complete pipeline—from data collection and pre-rocessing to model training, evaluation, and real-time inference—so you can understand how modern AI can fight misinformation. ✅ What you’ll learn What “fake content” means: deepfake video, face-swap, voice spoofing, image tampering Dataset workflow: data cleaning, labeling, augmentation Deep Learning models used: CNN / EfficientNet / ResNet, and optional LSTM/Transformer for video Feature extraction techniques: spatial artifacts + temporal inconsistencies Explainability: Grad-CAM / attention maps to show why content is flagged Performance metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix Deployment idea: API / web app / mobile integration for verification 🔍 Key Highlights (VERIFAL) Multi-type detection (Image + Video + optional Audio/Text) Robust against compression, resizing, and social-media reuploads Scalable architecture for real-world verification systems Output: REAL / FAKE confidence score + visual explanation 🌐 Use Cases Social media misinformation control Digital forensics & cybercrime investigation News/media verification pipelines Identity protection & deepfake prevention ⚠️ Disclaimer: This content is for education and research only. Always combine AI results with human verification for critical decisions. If you found this useful, Like • Share • Subscribe for more Deep Learning & Security projects! #DeepLearning #DeepFakeDetection #FakeDetection #AIForensics #ComputerVision #CyberSecurity #CNN #ResNet #EfficientNet #ExplainableAI #Misinformation