Sample Correlation for Fingerprinting Deep Face Recognition

• URL: http://arxiv.org/abs/2412.20768v1
• Date: Mon, 30 Dec 2024 07:37:06 GMT
• Title: Sample Correlation for Fingerprinting Deep Face Recognition
• Authors: Jiyang Guan, Jian Liang, Yanbo Wang, Ran He,
• Abstract summary: 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.
• Score: 83.53005932513156
• License:
• Abstract: Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques.However, an off-the-shelf face recognition model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner.Model fingerprinting, as a model stealing detection method, aims to verify whether a suspect model is stolen from the victim model, gaining more and more attention nowadays.Previous methods always utilize transferable adversarial examples as the model fingerprint, but this method is known to be sensitive to adversarial defense and transfer learning techniques.To address this issue, we consider the pairwise relationship between samples instead and propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC).Specifically, we present SAC-JC that selects JPEG compressed samples as model inputs and calculates the correlation matrix among their model outputs.Extensive results validate that 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.Furthermore, we extend our evaluation of SAC-JC to object recognition datasets including Tiny-ImageNet and CIFAR10, which also demonstrates the superior performance of SAC-JC to previous methods.The code will be available at \url{https://github.com/guanjiyang/SAC_JC}.

Related papers

• How to Backdoor Consistency Models? [10.977907906989342]
  We conduct the first study on the vulnerability of consistency models to backdoor attacks. Our proposed framework demonstrates the vulnerability of consistency models to backdoor attacks. Our framework successfully compromises the consistency models while maintaining high utility and specificity.
  arXiv  Detail & Related papers  (2024-10-14T22:25:06Z)
• FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification [10.911464455072391]
  FACTUAL is a Contrastive Learning framework for Adversarial Training and robust SAR classification. Our model achieves 99.7% accuracy on clean samples, and 89.6% on perturbed samples, both outperforming previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-04T06:20:22Z)
• 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)
• RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries [2.8532545355403123]
  Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. In this paper, we propose automatic face warping which needs extremely limited number of queries to fool the target model. We evaluate the robustness of proposed method in the decision-based black-box attack setting.
  arXiv  Detail & Related papers  (2022-07-04T00:22:45Z)
• Diffusion Models for Adversarial Purification [69.1882221038846]
  Adrial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. We propose DiffPure that uses diffusion models for adversarial purification. Our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods.
  arXiv  Detail & Related papers  (2022-05-16T06:03:00Z)
• 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)
• Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition [56.844587127848854]
  We demonstrate that the state-of-the-art gait recognition model is vulnerable to such attacks. We employ a generative adversarial network based architecture to semantically generate adversarial high-quality gait silhouettes or video frames. The experimental results show that if only one-fortieth of the frames are attacked, the accuracy of the target model drops dramatically.
  arXiv  Detail & Related papers  (2020-02-22T10:08:42Z)

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.