Fingerprinting Framework for EaaS Models

• Fingerprinting in EaaS models is a methodology that embeds unique identifiers into ML systems to guarantee ownership attribution and protect IP.
• It employs diverse methods—from weight-space modifications to geometric analysis—that remain robust under fine-tuning, compression, and adversarial transformations.
• Innovative techniques like perinucleus sampling and coded fingerprinting address scalability and collusion resistance, enabling effective black-box verification.

A fingerprinting framework for EaaS (Everything-as-a-Service) models refers to the set of methodologies and protocols enabling model owners and auditors to embed, extract, and verify unique identifying information (“fingerprints”) in remotely offered machine learning models. The primary objective is to ensure ownership attribution, verify provenance, and safeguard intellectual property in environments where only API or black-box access to models is available. Diverse fingerprinting paradigms have been developed, from embedding patterns in model weights or outputs to geometric/topological analyses, information-theoretic measures, and scalable protocols supporting collusion resistance and transformation robustness.

1. Foundational Principles of Fingerprinting in EaaS Models

Fingerprinting in EaaS systems addresses the challenge of securing intellectual property when deep learning models are distributed or consumed via APIs. Unlike watermarking—which typically injects backdoor triggers or special output patterns—fingerprinting is predicated on embedding or extracting user-unique markers that are robust to post-processing (fine-tuning, compression, quantization, merging), collusions, or adversarial threats.

Core design principles include:

• Stealthiness and Harmlessness: Fingerprints must not degrade model utility for benign tasks and should remain undetectable to unauthorized parties (Nasery et al., 30 Sep 2025).
• Robustness to Post-Transformation: Fingerprints should persist under operations such as fine-tuning, model compression, pruning, merging, and geometric transformations (Yamabe et al., 11 Oct 2024, Zhang et al., 19 Oct 2025).
• Scalability: Allow embedding thousands of unique fingerprints across distributed model copies without significant overhead or loss (Nasery et al., 11 Feb 2025).
• Black-box Extraction: Enable verification without internal access—via querying model outputs or embeddings (Chen et al., 2018, Yamabe et al., 11 Oct 2024, Maho et al., 2022).

2. Fingerprint Embedding and Extraction Methodologies

Several embedding and extraction techniques have been established:

Weight-Space Fingerprinting

The DeepMarks system (Chen et al., 2018) perturbs the probability density function (pdf) of trainable weights by introducing an additive loss during fine-tuning. For user $j$, an orthogonal or coded fingerprint $\mathbf{f}_j$ is defined (e.g., $\mathbf{f}_j = \mathbf{u}_j$ for orthogonal fingerprinting, or $\mathbf{f}_j = \sum_i b_{ij}\mathbf{u}_i$ for coded variants). The embedding loss:

$$L = L_0 + \gamma \cdot \mathrm{MSE}(\mathbf{f}_j - X\mathbf{w})$$

ensures the fingerprint is hidden yet retrievable. Extraction proceeds via correlation statistics between weights and the fingerprint codebook.

UAP-Based Global Decision Boundary Fingerprinting

Universal Adversarial Perturbations (UAPs) characterize a model’s decision boundary subspace (Peng et al., 2022). Fingerprints consist of response vectors $F(f, v, \{x_k\})$ to selected inputs and their UAP-perturbed variants. A contrastive encoder $E_\theta$ maps these to a latent space, quantifying similarity between victim and suspect models by cosine score, revealing high-confidence IP theft with minimal queries.

Benign Input Family-Based Fingerprinting

By leveraging only unmodified, “benign” inputs and observing top-$k$ model outputs (Maho et al., 2022), a statistical fingerprint is built via empirical mutual information:

$$\hat{I}(\tilde{Z}, \tilde{Y}) = \sum_{\tilde{z}, \tilde{y}} \hat{P}_{\tilde{Z}, \tilde{Y}}(\tilde{z}, \tilde{y}) \log\left( \frac{ \hat{P}_{\tilde{Z}, \tilde{Y}}(\tilde{z}, \tilde{y}) }{ \hat{P}_{\tilde{Z}}(\tilde{z}) \hat{P}_{\tilde{Y}}(\tilde{y}) } \right )$$

This supports both detection (binary hypothesis) and identification (multi-class family attribution).

Geometric and Topological Analysis

Recent frameworks (Zhang et al., 19 Oct 2025) treat model embeddings as point clouds in high-dimensional space. Ownership verification is achieved by spatially aligning suspect and victim point clouds under arbitrary rotation, scaling, and translation, and assessing similarity statistically:

$$\Delta_{AE} = \min_{R_e, \alpha_e, d_e} \frac{1}{N} \sum_{i=1}^N \| \alpha_e R_e p_i + d_e - q_i \|^2_2$$

3. Robustness Against Attacks and Transformations

Resilience is evaluated against several threat models:

• Collusion Attacks: Coded fingerprinting (e.g., BIBD AND-ACC codebooks) allows unique source identification even when several models are averaged (Chen et al., 2018, Nasery et al., 11 Feb 2025).
• Model Transformations: Experiments confirm fingerprint persistence under fine-tuning, pruning, compression, quantization, and adversarial training in multiple frameworks (Chen et al., 2018, Peng et al., 2022, Maho et al., 2022).
• Model Merging: MergePrint explicitly optimizes fingerprints for survival post-parameter merging by simulating pseudo-merged models during training stages (Yamabe et al., 11 Oct 2024).
• Geometric Attacks: Geometric/topological fingerprints are inherently resistant to rotation, scaling, and translation transformations and do not require training process modification (Zhang et al., 19 Oct 2025).
• Adaptive Adversarial Threats: Systematic attacks targeting verbatim matching, statistical signature leakage, and query anomalies can defeat naive fingerprint schemes unless the design incorporates stealth, cryptographically inspired verification, and diversified low-level signals (Nasery et al., 30 Sep 2025).

4. Scalability, Efficiency, and Evaluation Metrics

Fingerprint frameworks must efficiently scale, both in terms of embedded keys and verification rates:

• Perinucleus Sampling: This technique supports embedding up to 24,576 fingerprints in LLMs (demonstrated on Llama-3.1-8B) by judicious sampling from the “edge” of the nucleus in the token probability distribution, balancing rarity and in-distribution plausibility (Nasery et al., 11 Feb 2025).
• AKH Baseline and QuRD Framework: The Anna Karenina Heuristic (AKH) and systematic Query, Representation, Detection (QuRD) decomposition enable lightweight, yet effective, fingerprint generation and verification—even outperforming complex state-of-the-art approaches (Godinot et al., 17 Dec 2024).
• Metrics: True Positive Rate (TPR@FPR<5%) and False Positive Rate are standard; robustness is measured under both positive (stolen) and negative (benign/unrelated) model pairs.

Fingerprinting Scheme	Scalability	Collusion Resistance	Robustness to Transformation	Black-box Ready
DeepMarks (Chen et al., 2018)	Moderate	Yes	Yes	Yes
MergePrint (Yamabe et al., 11 Oct 2024)	Moderate	Merge-resistant	Yes	Yes
Perinucleus (Nasery et al., 11 Feb 2025)	High	Yes	Yes	Yes
Geometric (Zhang et al., 19 Oct 2025)	n/a	n/a	RST-resilient	Yes
Benign Input (Maho et al., 2022)	Moderate	Family-level	Yes	Yes

5. Practical Applications and Deployment Scenarios

Fingerprinting frameworks have found utility in various real-world contexts:

• Digital Rights Management (DRM): Each distributed or API-hosted model copy is uniquely attributed, enabling model owners to identify the source of unauthorized redistribution or use (Chen et al., 2018).
• Ownership and Provenance Verification: Robust black-box verification (MergePrint, geometric point clouds) supports legal enforcement and contractual compliance in EaaS systems (Yamabe et al., 11 Oct 2024, Zhang et al., 19 Oct 2025).
• Vendor Auditing and Monitoring: Hybrid static-dynamic fingerprinting frameworks support ongoing model tracking across SaaS and multi-agent GenAI deployments (Bhardwaj et al., 30 Jan 2025).
• Secure Device Authentication: VFDT-based multifractal fingerprints allow for robust network access control in dynamic edge/service environments (Johnson et al., 2023).
• Regulatory Compliance: Gradient-based and family classification methods bridge the technical gap for software-inspired IP tracking in LLM ecosystems (Wu et al., 2 Jun 2025).

6. Limitations, Security Considerations, and Future Directions

Challenges and recommendations highlighted in recent research include:

• Stealth and Query Indistinguishability: Fingerprint queries must be statistically indistinguishable from regular traffic to avoid filtering by adversaries (Nasery et al., 30 Sep 2025).
• Non-Verbatim, Distributed Signals: Design should avoid reliance on exact response matching or shallow n-gram statistics, favoring deeply embedded or cryptographically inspired signals (Nasery et al., 30 Sep 2025).
• Robust Verification Protocols: Verification should aggregate multi-signal, multi-layer, or statistical evidence, reducing susceptibility to targeted suppression or obfuscation (Godinot et al., 17 Dec 2024).
• Scalability versus Collusion: Theoretical analyses indicate that required fingerprint count increases exponentially with coalition size—implying practical coalition limits and a need for adaptive/flexible scaling (Nasery et al., 11 Feb 2025).
• Adaptive Defense: Recommendations include adversarial training, automated multi-agent monitoring, and periodic fingerprint updates to match evolving threat landscapes (Bhardwaj et al., 30 Jan 2025, Nasery et al., 30 Sep 2025).

7. Research Ecosystem and Benchmarking

The field’s progress is further catalyzed by:

• Open-source benchmarking toolkits supporting systematic evaluation of $\sim$100 fingerprinting QuRD combinations across datasets and model architectures (Godinot et al., 17 Dec 2024).
• Rigorous experimental validation involving thousands of model variants, API-only access conditions, real-world datasets, and statistical testing (Zhang et al., 19 Oct 2025, Maho et al., 2022).
• Integration of software engineering practices such as centroid-initialized clustering for family attribution in LLMs, facilitating regulatory compliance (Wu et al., 2 Jun 2025).

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Fingerprinting frameworks for EaaS models provide rigorously validated, multi-dimensional solutions for intellectual property protection, usage monitoring, and ownership verification. By advancing beyond watermarking—leveraging robust embedding, extraction, and verification methods resilient to both benign and adversarial transformation—they address the critical needs of modern cloud and API-driven machine learning environments.