Day 21 Model Inversion
Model Inversion Attacks – Reconstructing Faces from ML Models 🤯
Imagine asking an AI model about diabetes risk — and reconstructing a patient’s face. That’s not sci-fi. That’s model inversion — and it’s happening now.
🔍 What Is Model Inversion?
Model Inversion Attacks aim to reconstruct sensitive input features (like faces, DNA, or text) using a model’s outputs — especially when it’s overfit or exposes confidence scores.
🧠 When trained on PII-rich data, models can unintentionally leak individual details from the training set.
🧪 Real-World Example
📚 Fredrikson et al. (2015):
Trained a model to predict warfarin dosage.
By probing the model with known inputs, they reconstructed genetic markers of real patients.
🎭 Facial Recognition: Similar attacks recreated faces from facial recognition models — just by analyzing output scores.
🧠 Different Models, Different Risks
🖼️ Image Models: Attackers can recreate training images.
📄 Text Models (LLMs): May regenerate secrets, passwords, or emails.
📊 Graph Models: Can leak node attributes or private edges.
🔐 Defenses (Summary)
✅ Don’t expose confidence scores or logits publicly. ✅ Train with differential privacy. ✅ Use dropout/regularization to reduce memorization. ✅ Monitor for unusual query patterns (e.g., mass probing).
❗ Model Inversion vs Membership Inference
Model Inversion
Rebuild sensitive data
Model outputs
Reconstructed inputs
Membership Inference
Detect if data was in training
Model + datapoint
Yes / No
💬 Thought Starter
Would you expose top-5 predictions with confidence scores in production? How do you balance model utility vs user privacy?
🔗 Resources
📙 Recap of Day 1–20: LinkedIn Post
📘 GitBook (All Posts): 100 Days of AI Sec
🏷️ Tags
#100DaysOfAISec #AISecurity #MLSecurity #ModelInversion #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #MachineLearningSecurity #100DaysChallenge #ArifLearnsAI #LinkedInTech
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