The ABC of a Passport to Protecting Low-Rank Weights: SEAL and Intellectual Property Rights in AI

 The ABC of a Passport to Protecting Low-Rank Weights: SEAL and Intellectual Property Rights in AI

In the rapidly advancing world of artificial intelligence, intellectual property rights are becoming a cornerstone of ethical AI development. The sheer cost of training large models and fine-tuning them for specific tasks makes these parameters valuable intellectual property (IP). Without robust protection mechanisms, these assets can be misused, leading to financial and reputational losses.

Among the popular tools for fine-tuning large models is Low-Rank Adaptation (LoRA), which achieves efficient parameter adjustment without affecting the original pretrained weights. However, the widespread open sharing of LoRA weights leaves them vulnerable to IP theft. To address this challenge, SEAL (Secure Watermarking on LoRA weights) introduces a robust watermarking mechanism by embedding a non-trainable matrix, called a passport, into the model's weights. SEAL ensures ownership protection without compromising model performance.

The SEAL Training Algorithm

SEAL secures ownership by entangling the passport matrix with LoRA’s trainable parameters. Here is the precise training algorithm, including its mathematical details.

Algorithm 1: SEAL Training Procedure

Input:
Pretrained weights W
LoRA rank r
Passports C, Cp (non-trainable matrices)
Training dataset D, number of epochs E

Output:
Public LoRA weights B', A'
Private parameters B, A, C, Cp

1. Initialize A in R^(r x a), B in R^(b x r) as trainable parameters. Set C, Cp in R^(r x r) as non-trainable passports.
2. For each epoch e = 1 to E:
   For each batch (x, y) in D:
   Randomly select C or Cp.
   Compute the updated weights:
   W' = W + BCA or W' = W + BCpA
   Compute the loss:
   L(W', x, y)
   Backpropagate gradients:
   ∇L
3. Decompose C into two components C1, C2:
   C = C1C2
4. Modify the trainable parameters by incorporating the decomposed components:
   B' = BC1, A' = C2A
5. Return the publicly shareable weights B', A', while keeping B, A, C, Cp private.

Commentary on the SEAL Training Algorithm

Entanglement: By introducing the passport C, SEAL ensures that the trainable parameters B and A are interdependent on C. This entanglement guarantees that the model cannot function correctly without the hidden passport.
Decomposition: The decomposition step hides the passport within B' and A', making it invisible to adversaries while preserving its ability to validate ownership.
Performance Integrity: The model’s performance remains unaffected as the passports are seamlessly integrated without adding additional constraints or loss terms.

The SEAL Verification Algorithms

SEAL includes two complementary verification methods: extraction-based and fidelity-based. These methods enable model owners to validate their claims of ownership.

Algorithm 2: SEAL Verification by Extraction

Input:
Public weights A', B'
Claimed original parameters A, B, C

Output:
Ownership status (True/False)

1. Reconstruct the passport:
   Cext = B† B'A' A†
   where B† and A† are the pseudoinverses of B and A.
2. Compare Cext with the claimed passport C.
   If Cext ≈ C, return True.
   Otherwise, return False.

Algorithm 3: SEAL Verification by Fidelity

Input:
Suspected weights B', A'
Claimed original parameters B, A, Ca, Cb
Threshold εT
Task T
Metric MT (performance measure on task T)

Output:
Ownership status (True/False)

1. Verify if the claimed parameters reconstruct the suspected weights:
   B Ca A = B' A'
   If not, return False.
2. Compute the fidelity gap:
   Δ = |MT(B, A, Ca) - MT(B, A, Cb)|
3. Compare the fidelity gap with the threshold:
   If Δ ≤ εT, return True.
   Otherwise, return False.

Commentary on Verification Algorithms

Extraction-Based Verification: This method directly reconstructs the passport from the suspected weights, relying on the statistical equivalence between the reconstructed and claimed passports. It provides clear and direct evidence of ownership.
Fidelity-Based Verification: This approach uses the model’s performance as a proxy for ownership. By testing the model with the correct and alternative passports, it evaluates whether the entanglement during training is unique to the claimed parameters.

The Importance of SEAL

SEAL represents a significant step forward in safeguarding AI intellectual property. By embedding robust, verifiable watermarks into LoRA weights, SEAL provides model owners with the tools to assert and protect their ownership rights. The dual verification mechanisms ensure that SEAL is both effective and resilient against adversarial attacks.

For detailed insights and experimental validation, refer to the original SEAL paper at http://arxiv.org/abs/2501.09284.