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.

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🔍 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.

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🧪 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.

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🧠 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.

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🔐 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).

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❗ Model Inversion vs Membership Inference

Attack Type	Goal	Input	Output
Model Inversion	Rebuild sensitive data	Model outputs	Reconstructed inputs
Membership Inference	Detect if data was in training	Model + datapoint	Yes / No

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💬 Thought Starter

Would you expose top-5 predictions with confidence scores in production? How do you balance model utility vs user privacy?

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🔗 Resources

• 📙 Recap of Day 1–20: LinkedIn Post

• 📘 GitBook (All Posts): 100 Days of AI Sec

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🏷️ Tags

#100DaysOfAISec #AISecurity #MLSecurity #ModelInversion #CyberSecurity #AIPrivacy #AdversarialML #LearningInPublic #MachineLearningSecurity #100DaysChallenge #ArifLearnsAI #LinkedInTech