Day 16 Deep vs Shallow Models

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Day 16 Poster

🔍 Shallow vs. Deep Models — What’s the Difference?

🧠 Shallow Models (1–2 layers)

Think logistic regression, decision trees, or simple SVMs. ✅ Easy to interpret ✅ Less prone to overfitting (especially with small datasets) ❌ Limited ability to capture complex patterns

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🧠 Deep Models (3+ hidden layers)

These are your typical neural networks used in vision, NLP, and more. ✅ Excellent at learning abstract and nonlinear features ✅ Essential for modern AI tasks ❌ More prone to overfitting, adversarial attacks, and memorization risks

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🔐 Security Lens — Where Things Break

1️⃣ Overfitting to Poisoned Data (Deep Models)

Deep models can memorize malicious training examples. 💥 Example: Poison a single face in a dataset → model always misidentifies that person.

2️⃣ Gradient-Based Attacks

Deep models expose smooth gradients, perfect for crafting adversarial inputs. 💥 Example: Slight pixel tweaks make a CNN label a “cat” as “guacamole.”

3️⃣ Explainability Gaps

Deep models = black boxes. Hard to reason about decisions → hard to audit for security. 💥 Risk: Hidden backdoors via neuron triggers or malicious weight updates.

4️⃣ Robustness Trade-Offs (Shallow Models)

Shallow models are more interpretable, but fragile in high-dimensional spaces. 💥 Example: A basic spam detector using logistic regression can be tricked with obfuscation (like “fr33 mon3y!”)

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🧩 What Can Help?

• Defensive distillation

• Adversarial training

• Model explainability tools (e.g., SHAP, LIME)

• Regular audits of training data sources

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📚 References

• Ilyas et al. (2019) — “Adversarial Examples Are Not Bugs, They Are Features”

• Madry et al. (2018) — “Towards Deep Learning Models Resistant to Adversarial Attacks”

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💬 Question for You

Have you ever chosen a shallow model over a deep one to make it easier to audit or secure?

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📅 Tomorrow: Hyperparameter Tuning — How optimizing accuracy can accidentally expose your model to attacks.

🔗 Missed Day 15? https://lnkd.in/dMrvK_yt

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