VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints

• URL: http://arxiv.org/abs/2310.10656v1
• Date: Thu, 7 Sep 2023 01:58:12 GMT
• Title: VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints
• Authors: Aoting Hu, Zhigang Lu, Renjie Xie, Minhui Xue
• Abstract summary: Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. We propose a novel ownership testing method called VeriDIP, which verifies a model's intellectual property.
• Score: 16.564206424838485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. However, previous works often rely on overfitting or robustness features as fingerprints, lacking theoretical guarantees and exhibiting under-performance on generalized models. In this paper, we propose a novel ownership testing method called VeriDIP, which verifies a DNN model's intellectual property. VeriDIP makes two major contributions. (1) It utilizes membership inference attacks to estimate the lower bound of privacy leakage, which reflects the fingerprint of a given model. The privacy leakage fingerprints highlight the unique patterns through which the models memorize sensitive training datasets. (2) We introduce a novel approach using less private samples to enhance the performance of ownership testing. Extensive experimental results confirm that VeriDIP is effective and efficient in validating the ownership of deep learning models trained on both image and tabular datasets. VeriDIP achieves comparable performance to state-of-the-art methods on image datasets while significantly reducing computation and communication costs. Enhanced VeriDIP demonstrates superior verification performance on generalized deep learning models, particularly on table-trained models. Additionally, VeriDIP exhibits similar effectiveness on utility-preserving differentially private models compared to non-differentially private baselines.

Related papers

• A Behavioral Fingerprint for Large Language Models: Provenance Tracking via Refusal Vectors [43.11304710234668]
  We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment.<n>In a large-scale identification task across 76 offspring models, our method achieves 100% accuracy in identifying the correct base model family.<n>We propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact.
  arXiv  Detail & Related papers  (2026-02-10T05:57:35Z)
• Antidistillation Fingerprinting [119.66677613290359]
  We introduce antidistillation fingerprinting (ADFP), a principled approach that aligns the fingerprinting objective with the student's learning dynamics.<n>ADFP achieves a significant improvement over state-of-the-art baselines, stronger detection confidence with minimal impact on utility, even when the student model's architecture is unknown.
  arXiv  Detail & Related papers  (2026-02-03T18:15:50Z)
• SELF: A Robust Singular Value and Eigenvalue Approach for LLM Fingerprinting [4.335948336782789]
  We propose a novel intrinsic weight-based fingerprinting scheme that eliminates dependency on input and inherently resists false claims.<n> SELF achieves robust IP protection through two key innovations: 1) unique, scalable and transformation-invariant fingerprint extraction via singular value and eigenvalue decomposition of LLM attention weights, and 2) effective neural network-based fingerprint similarity comparison based on few-shot learning and data augmentation.
  arXiv  Detail & Related papers  (2025-12-03T09:53:47Z)
• 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)
• SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From [65.75182441010327]
  We propose a stronger and more intrinsic notion of LLM fingerprinting: SeedPrints.<n>We show that untrained models exhibit reproducible token selection biases conditioned solely on their parameters.<n> Experiments on LLaMA-style and Qwen-style models show that SeedPrints achieves seed-level distinguishability and can provide birth-to-lifecycle identity verification akin to a biometric fingerprint.
  arXiv  Detail & Related papers  (2025-09-30T15:34:08Z)
• Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! [1.8824463630667776]
  Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse becomes prevalent.<n>This work introduces a simple yet effective approach for robust fingerprinting based on intrinsic model characteristics.
  arXiv  Detail & Related papers  (2025-07-02T12:29:38Z)
• Holmes: Towards Effective and Harmless Model Ownership Verification to Personalized Large Vision Models via Decoupling Common Features [54.63343151319368]
  This paper proposes a harmless model ownership verification method for personalized models by decoupling similar common features.<n>In the first stage, we create shadow models that retain common features of the victim model while disrupting dataset-specific features.<n>After that, a meta-classifier is trained to identify stolen models by determining whether suspicious models contain the dataset-specific features of the victim.
  arXiv  Detail & Related papers  (2025-06-24T15:40:11Z)
• 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. 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 [29.015707553430442]
  Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models. In this paper, we reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model. 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)
• Sample Correlation for Fingerprinting Deep Face Recognition [83.53005932513156]
  We propose a novel model stealing detection method based on SA Corremplelation (SAC) SAC successfully defends against various model stealing attacks in deep face recognition, encompassing face verification and face emotion recognition, exhibiting the highest performance in terms of AUC, p-value and F1 score. We extend our evaluation of SAC-JC to object recognition including Tiny-ImageNet and CIFAR10, which also demonstrates the superior performance of SAC-JC to previous methods.
  arXiv  Detail & Related papers  (2024-12-30T07:37:06Z)
• Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models [23.09033991200197]
  New personalization techniques have been proposed to customize the pre-trained base models for crafting images with specific themes or styles. Such a lightweight solution poses a new concern regarding whether the personalized models are trained from unauthorized data. We introduce SIREN, a novel methodology to proactively trace unauthorized data usage in black-box personalized text-to-image diffusion models.
  arXiv  Detail & Related papers  (2024-10-14T12:29:23Z)
• IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method [8.717704777664604]
  Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. We propose IDEA, an Inverse Domain Expert Adaptation based proactive DNN IP protection method featuring active authorization and source traceability. We extensively evaluate IDEA on five datasets and four DNN models to demonstrate its effectiveness in authorization control, culprit tracing success rate, and against various attacks.
  arXiv  Detail & Related papers  (2024-09-29T09:34:33Z)
• EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations [73.94175015918059]
  We introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing.
  arXiv  Detail & Related papers  (2024-06-20T02:02:44Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data [7.203557048672379]
  We present FPGAN-Control, an identity preserving image generation framework. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity level, degree of appearance control, and low synthetic-to-real domain gap.
  arXiv  Detail & Related papers  (2023-10-29T14:30:01Z)
• Independent Distribution Regularization for Private Graph Embedding [55.24441467292359]
  Graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged. We propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term.
  arXiv  Detail & Related papers  (2023-08-16T13:32:43Z)
• Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification [15.88604823470663]
  The presented scheme is applied in offline handwritten signature verification (OffSV) The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD) Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets.
  arXiv  Detail & Related papers  (2023-08-02T13:28:12Z)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)
• Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation [100.69458267888962]
  Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. Due to legal and privacy issues, training data (real face images and spoof images) are not allowed to be directly shared between different data sources. We propose a Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation framework.
  arXiv  Detail & Related papers  (2021-10-25T02:51:05Z)
• FedIPR: Ownership Verification for Federated Deep Neural Network Models [31.459374163080994]
  Federated learning models must be protected against plagiarism since these models are built upon valuable training data owned by multiple institutions or people. This paper illustrates a novel federated deep neural network (FedDNN) ownership verification scheme that allows ownership signatures to be embedded and verified to claim legitimate intellectual property rights (IPR) of FedDNN models.
  arXiv  Detail & Related papers  (2021-09-27T12:51:24Z)
• A Multi-Level Attention Model for Evidence-Based Fact Checking [58.95413968110558]
  We present a simple model that can be trained on sequence structures. Results on a large-scale dataset for Fact Extraction and VERification show that our model outperforms the graph-based approaches.
  arXiv  Detail & Related papers  (2021-06-02T05:40:12Z)

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.