model-extraction-relu-logits（模型提取 ReLU Logits）

Model Extraction for ReLU Networks

This skill provides guidance for extracting internal weight matrices from black-box ReLU neural networks using only input-output access.

Problem Understanding

Model extraction tasks typically involve:

• A black-box neural network that accepts inputs and returns outputs (logits)
• The goal of recovering internal parameters (weight matrices, biases)
• No direct access to the network's implementation or internal state

Critical Principle: True Black-Box Treatment

Treat the target network as a genuine black-box. Never rely on implementation details that may change during evaluation:

• Do not hardcode hidden layer dimensions from example code
• Do not assume specific random seeds or initialization schemes
• Do not directly compare extracted weights to "true" weights read from source files
• The test environment may use completely different parameters than any provided examples

Approach Selection

Understanding ReLU Network Structure

A two-layer ReLU network computes: output = A2 @ ReLU(A1 @ x + b1) + b2

Key properties to exploit:

1. Piecewise linearity: ReLU networks are piecewise linear functions
2. Activation boundaries: Each hidden neuron creates a hyperplane boundary where its output transitions from zero to active
3. Gradient structure: In each linear region, the gradient reveals information about active neurons

Recommended Extraction Strategies

Strategy 1: Critical Point Analysis

ReLU networks have critical points where neurons transition between active/inactive states:

1. Probe the network systematically to identify transition boundaries
2. At each boundary, a hyperplane normal corresponds to a row of A1
3. Collect enough boundaries to reconstruct A1

Strategy 2: Gradient-Based Extraction

For networks where gradients are accessible or can be approximated:

1. Query gradients at multiple random points
2. Gradients in a linear region reveal which neurons are active
3. Use gradient information to identify weight matrix rows

Strategy 3: Activation Pattern Enumeration

Systematically identify which neurons are active in different input regions:

1. Start from a known point and identify its activation pattern
2. Search for inputs that cause different neurons to activate
3. Use the transition points to extract hyperplane parameters

Strategy 4: Optimization-Based Fitting (Fallback)

When mathematically principled methods are insufficient:

1. Generate diverse input-output pairs from the black-box
2. Train a surrogate network to match outputs
3. Critical: Make network capacity adaptive (try multiple hidden dimensions)
4. Validate by output matching, not parameter comparison

Hidden Dimension Discovery

Since the hidden dimension is unknown, employ detection strategies:

1. Rank analysis: The output dimension and response complexity bound hidden size
2. Binary search: Try different hidden sizes and measure reconstruction error
3. Overcomplete fitting: Use larger hidden dimension than necessary, then identify redundant neurons
4. Gradient counting: In a fixed input region, count distinct gradient patterns

Verification Strategy

Correct Verification (Functional Equivalence)

# Generate test inputs NOT used during extraction
test_inputs = generate_diverse_inputs(n=1000)

# Compare outputs
original_outputs = [black_box_query(x) for x in test_inputs]
extracted_outputs = [extracted_model(x) for x in test_inputs]

# Check functional equivalence
max_error = max(|original - extracted| for all test points)
assert max_error < tolerance

Incorrect Verification (Avoid These)

• Comparing extracted weights directly to weights read from source files
• Using the same inputs for extraction and verification
• Relying on cosine similarity to "true" parameters
• Checking only a small number of test points

Common Pitfalls

1. Peeking at Implementation Details

Problem: Reading source code to get the "true" weights or hidden dimension, then validating against them.

Why it fails: Test environments often use different parameters (different seeds, dimensions, scales).

Solution: Treat extraction as if source code doesn't exist. Validate only through output comparison.

2. Hardcoding Network Architecture

Problem: Assuming hidden dimension is fixed (e.g., n_neurons=20).

Why it fails: The actual network may have a different architecture.

Solution: Either detect hidden dimension empirically or design extraction to work with unknown dimensions.

3. Non-Unique Solutions

Problem: Many weight configurations produce identical input-output behavior.

Why it fails: Optimization may find a valid equivalent representation, not the original weights.

Solution: If the task requires recovering *specific* original weights (not just functional equivalents), use mathematically principled extraction that exploits ReLU structure.

4. Insufficient Test Coverage

Problem: Verifying on a few hand-picked inputs.

Why it fails: The extracted model may fail on untested input regions.

Solution: Use comprehensive random testing across the input domain, including edge cases.

5. Numerical Precision Issues

Problem: Accumulated floating-point errors cause extraction to fail.

Solution: Use numerically stable algorithms, appropriate tolerances, and verify with realistic precision expectations.

Implementation Checklist

Before declaring success, verify:

• No implementation details (seeds, dimensions) were read from source files
• Hidden dimension was detected or handled adaptively
• Verification uses only input-output comparisons
• Verification inputs are independent from extraction inputs
• Sufficient test coverage (hundreds to thousands of points)
• Error tolerance is appropriate for the task requirements
• The extracted model works as a functional replacement

When Standard Approaches Fail

If initial extraction attempts fail:

1. Increase probe density: More input-output pairs may be needed
2. Try multiple hidden dimensions: The assumed size may be wrong
3. Check for numerical issues: Scaling, precision, or conditioning problems
4. Verify the network structure: Ensure assumptions about architecture (two-layer, ReLU) are correct
5. Consider alternative representations: Some equivalent parameterizations may be easier to extract