Lossless Copyright Protection via Intrinsic Model Fingerprinting

• URL: http://arxiv.org/abs/2601.21252v1
• Date: Thu, 29 Jan 2026 04:18:07 GMT
• Title: Lossless Copyright Protection via Intrinsic Model Fingerprinting
• Authors: Lingxiao Chen, Liqin Wang, Wei Lu, Xiangyang Luo,
• Abstract summary: Existing protection methods modify the model to embed watermarks, which impairs performance.<n>We propose TrajPrint, a completely lossless and training-free framework that verifies model copyright by extracting unique manifold fingerprints.
• Score: 21.898748690761874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exceptional performance of diffusion models establishes them as high-value intellectual property but exposes them to unauthorized replication. Existing protection methods either modify the model to embed watermarks, which impairs performance, or extract model fingerprints by manipulating the denoising process, rendering them incompatible with black-box APIs. In this paper, we propose TrajPrint, a completely lossless and training-free framework that verifies model copyright by extracting unique manifold fingerprints formed during deterministic generation. Specifically, we first utilize a watermarked image as an anchor and exactly trace the path back to its trajectory origin, effectively locking the model fingerprint mapped by this path. Subsequently, we implement a joint optimization strategy that employs dual-end anchoring to synthesize a specific fingerprint noise, which strictly adheres to the target manifold for robust watermark recovery. As input, it enables the protected target model to recover the watermarked image, while failing on non-target models. Finally, we achieved verification via atomic inference and statistical hypothesis testing. Extensive experiments demonstrate that TrajPrint achieves lossless verification in black-box API scenarios with superior robustness against model modifications.

Related papers

• Adapter Shield: A Unified Framework with Built-in Authentication for Preventing Unauthorized Zero-Shot Image-to-Image Generation [74.5813283875938]
  Zero-shot image-to-image generation poses substantial risks related to intellectual property violations.<n>This work presents Adapter Shield, the first universal and authentication-integrated solution aimed at defending personal images from misuse.<n>Our method surpasses existing state-of-the-art defenses in blocking unauthorized zero-shot image synthesis.
  arXiv  Detail & Related papers  (2025-11-25T04:49:16Z)
• SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
  We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-11-05T13:48:48Z)
• Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models [52.877452505561706]
  We propose the first copyright evasion attack specifically designed to undermine dataset ownership verification (DOV)<n>Our CEAT2I comprises three stages: watermarked sample detection, trigger identification, and efficient watermark mitigation.<n>Our experiments show that our CEAT2I effectively evades DOV mechanisms while preserving model performance.
  arXiv  Detail & Related papers  (2025-05-05T17:51:55Z)
• AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection [26.066755429896926]
  Methods select Out-of-Distribution (OoD) data as backdoor watermarks and retrain the original model for copyright protection.<n>Existing methods are susceptible to malicious detection and forgery by adversaries, resulting in watermark evasion.<n>We propose Model-underlineagnostic Black-box Backdoor Wunderlineatermarking Framework (AGATE) to address stealthiness and robustness challenges in multimodal model copyright protection.
  arXiv  Detail & Related papers  (2025-04-28T14:52:01Z)
• Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.<n>We propose a diffusion model watermarking method tailored for real-world deployment.<n>Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
  arXiv  Detail & Related papers  (2025-04-21T11:18:16Z)
• Adversarial Example Based Fingerprinting for Robust Copyright Protection in Split Learning [17.08424946015621]
  We propose the first copyright protection scheme for Split Learning model, leveraging fingerprint to ensure effective and robust copyright protection.<n>This is demonstrated by a remarkable fingerprint verification success rate (FVSR) of 100% on MNIST, 98% on CIFAR-10, and 100% on ImageNet.
  arXiv  Detail & Related papers  (2025-03-05T06:07:16Z)
• FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint [22.398234847594242]
  Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models.<n>In this paper, we reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model.<n>Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks.
  arXiv  Detail & Related papers  (2025-01-26T13:00:58Z)
• ModelShield: Adaptive and Robust Watermark against Model Extraction Attack [58.46326901858431]
  Large language models (LLMs) demonstrate general intelligence across a variety of machine learning tasks.<n> adversaries can still utilize model extraction attacks to steal the model intelligence encoded in model generation.<n> Watermarking technology offers a promising solution for defending against such attacks by embedding unique identifiers into the model-generated content.
  arXiv  Detail & Related papers  (2024-05-03T06:41:48Z)
• Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion [15.086451828825398]
  evasion adversaries can readily exploit the shortcuts created by models memorizing watermark samples. By learning the model to accurately recognize them, unique watermark behaviors are promoted through knowledge injection.
  arXiv  Detail & Related papers  (2024-04-21T03:38:20Z)
• Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models [71.13610023354967]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. We propose a diffusion model watermarking technique that is both performance-lossless and training-free.
  arXiv  Detail & Related papers  (2024-04-07T13:30:10Z)
• Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
  Training a high-performance deep neural network requires large amounts of data and computational resources. We propose a safe and robust backdoor-based watermark injection technique. We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
  arXiv  Detail & Related papers  (2023-09-04T19:58:35Z)
• DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking [3.282282297279473]
  The functionality of a deep learning (DL) model can be stolen via model extraction. We propose a novel watermarking technique called DynaMarks to protect the intellectual property (IP) of DL models.
  arXiv  Detail & Related papers  (2022-07-27T06:49:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.