ModelLock: Locking Your Model With a Spell

• URL: http://arxiv.org/abs/2405.16285v3
• Date: Sun, 13 Oct 2024 17:20:03 GMT
• Title: ModelLock: Locking Your Model With a Spell
• Authors: Yifeng Gao, Yuhua Sun, Xingjun Ma, Zuxuan Wu, Yu-Gang Jiang,
• Abstract summary: A diffusion-based framework dubbed ModelLock explores text-guided image editing to transform the training data into unique styles or add new objects in the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the text prompt used to transform the data. We conduct extensive experiments on both image classification and segmentation tasks, and show that ModelLock can effectively lock the finetuned models without significantly reducing the expected performance.
• Score: 90.36433941408536
• License:
• Abstract: This paper presents a novel model protection paradigm ModelLock that locks (destroys) the performance of a model on normal clean data so as to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the training data into unique styles or add new objects in the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the text prompt used to transform the data. We conduct extensive experiments on both image classification and segmentation tasks, and show that 1) ModelLock can effectively lock the finetuned models without significantly reducing the expected performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models.

Related papers

• Stealth edits to large language models [76.53356051271014]
  We show that a single metric can be used to assess a model's editability. We also reveal the vulnerability of language models to stealth attacks.
  arXiv  Detail & Related papers  (2024-06-18T14:43:18Z)
• Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation [33.60710287553274]
  We propose to copy black-box classification models without having access to the original training data, the architecture, and the weights of the model. We employ a novel active self-paced learning framework to make the most of the proxy data during distillation. Our empirical results on two data sets confirm the superiority of our framework over two state-of-the-art methods in the few-call model extraction scenario.
  arXiv  Detail & Related papers  (2023-09-29T19:09:27Z)
• Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks [86.55317144826179]
  Previous methods always leverage the transferable adversarial examples as the model fingerprint. We propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC) SAC successfully defends against various model stealing attacks, even including adversarial training or transfer learning.
  arXiv  Detail & Related papers  (2022-10-21T02:07:50Z)
• MOVE: Effective and Harmless Ownership Verification via Embedded External Features [109.19238806106426]
  We propose an effective and harmless model ownership verification (MOVE) to defend against different types of model stealing simultaneously. We conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features. In particular, we develop our MOVE method under both white-box and black-box settings to provide comprehensive model protection.
  arXiv  Detail & Related papers  (2022-08-04T02:22:29Z)
• Defending against Model Stealing via Verifying Embedded External Features [90.29429679125508]
  adversaries can steal' deployed models even when they have no training samples and can not get access to the model parameters or structures. We explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified emphexternal features. Our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process.
  arXiv  Detail & Related papers  (2021-12-07T03:51:54Z)
• A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access [15.483078145498085]
  We propose a novel method for protecting convolutional neural network (CNN) models with a secret key set. The method enables us to protect not only from copyright infringement but also the functionality of a model from unauthorized access.
  arXiv  Detail & Related papers  (2021-05-31T07:37:33Z)
• Transfer Learning-Based Model Protection With Secret Key [15.483078145498085]
  We propose a novel method for protecting trained models with a secret key. In experiments with the ImageNet dataset, it is shown that the performance of a protected model was close to that of a non-protected model when the correct key was given.
  arXiv  Detail & Related papers  (2021-03-05T08:12:11Z)
• Training DNN Model with Secret Key for Model Protection [17.551718914117917]
  We propose a model protection method by using block-wise pixel shuffling with a secret key as a preprocessing technique to input images. Experiment results show that the performance of the protected model is close to that of non-protected models when the key is correct.
  arXiv  Detail & Related papers  (2020-08-06T04:25:59Z)
• Model Watermarking for Image Processing Networks [120.918532981871]
  How to protect the intellectual property of deep models is a very important but seriously under-researched problem. We propose the first model watermarking framework for protecting image processing models.
  arXiv  Detail & Related papers  (2020-02-25T18:36:18Z)

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.