Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks

• URL: http://arxiv.org/abs/2311.10389v1
• Date: Fri, 17 Nov 2023 08:35:02 GMT
• Title: Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
• Authors: Wenhao Wang, Guyue Li, Zhiming Chu, Haobo Li and Daniele Faccio
• Abstract summary: PUPGUARD is a solution designed to guard against puppet attacks. It is based on user behavioral patterns, specifically, the user needs to press the capture device twice successively with different fingers during the authentication process. Our experimental results demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a remarkably low false positive rate (FPR) of 1.89%.
• Score: 7.20329132808002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fingerprint traits are widely recognized for their unique qualities and security benefits. Despite their extensive use, fingerprint features can be vulnerable to puppet attacks, where attackers manipulate a reluctant but genuine user into completing the authentication process. Defending against such attacks is challenging due to the coexistence of a legitimate identity and an illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to guard against puppet attacks. This method is based on user behavioral patterns, specifically, the user needs to press the capture device twice successively with different fingers during the authentication process. PUPGUARD leverages both the image features of fingerprints and the timing characteristics of the pressing intervals to establish two-factor authentication. More specifically, after extracting image features and timing characteristics, and performing feature selection on the image features, PUPGUARD fuses these two features into a one-dimensional feature vector, and feeds it into a one-class classifier to obtain the classification result. This two-factor authentication method emphasizes dynamic behavioral patterns during the authentication process, thereby enhancing security against puppet attacks. To assess PUPGUARD's effectiveness, we conducted experiments on datasets collected from 31 subjects, including image features and timing characteristics. Our experimental results demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted comparative experiments to validate the superiority of combining image features and timing characteristics within PUPGUARD for enhancing resistance against puppet attacks.

Related papers

• AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
  Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks.
  arXiv  Detail & Related papers  (2024-08-04T09:53:50Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Joint Identity Verification and Pose Alignment for Partial Fingerprints [33.05877729161858]
  We propose a novel framework for joint identity verification and pose alignment of partial fingerprint pairs. Our method achieves state-of-the-art performance in both partial fingerprint verification and relative pose estimation.
  arXiv  Detail & Related papers  (2024-05-07T02:45:50Z)
• KDPrint: Passive Authentication using Keystroke Dynamics-to-Image Encoding via Standardization [7.251941112707364]
  This paper proposes a passive authentication system that utilizes keystroke data, a byproduct of primary authentication methods, for background user authentication. We introduce a novel image encoding technique to capture the temporal dynamics of keystroke data, overcoming the performance limitations of deep learning models. Experimental results demonstrate that the proposed imaging approach surpasses existing methods in terms of information capacity.
  arXiv  Detail & Related papers  (2024-05-02T08:18:37Z)
• ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning [57.91881829308395]
  Identity-preserving text-to-image generation (ID-T2I) has received significant attention due to its wide range of application scenarios like AI portrait and advertising. We present textbfID-Aligner, a general feedback learning framework to enhance ID-T2I performance.
  arXiv  Detail & Related papers  (2024-04-23T18:41:56Z)
• Dealing with Subject Similarity in Differential Morphing Attack Detection [4.3659108218579545]
  We focus on Differential MAD (D-MAD), where a trusted live capture, usually representing the criminal, is compared with the document image to classify it as morphed or bona fide. We show these approaches based on identity features are effective when the morphed image and the live one are sufficiently diverse. We propose ACIdA, a modular D-MAD system, consisting of a module for the attempt type classification, and two modules for the identity and artifacts analysis on input images.
  arXiv  Detail & Related papers  (2024-04-11T12:00:06Z)
• A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems [0.0]
  We propose a universal presentation attack detection method for contactless fingerprints. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions.
  arXiv  Detail & Related papers  (2023-10-23T15:46:47Z)
• Attribute-Guided Encryption with Facial Texture Masking [64.77548539959501]
  We propose Attribute Guided Encryption with Facial Texture Masking to protect users from unauthorized facial recognition systems. Our proposed method produces more natural-looking encrypted images than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T23:50:43Z)
• Leveraging Diffusion For Strong and High Quality Face Morphing Attacks [2.0795007613453445]
  Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image.
  arXiv  Detail & Related papers  (2023-01-10T21:50:26Z)
• A Comparative Study of Fingerprint Image-Quality Estimation Methods [54.84936551037727]
  Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. In this work, we review existing approaches for fingerprint image-quality estimation. We have also tested a selection of fingerprint image-quality estimation algorithms.
  arXiv  Detail & Related papers  (2021-11-14T19:53:12Z)
• Camera Invariant Feature Learning for Generalized Face Anti-spoofing [95.30490139294136]
  We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings.
  arXiv  Detail & Related papers  (2021-01-25T13:40:43Z)

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.