Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection

• URL: http://arxiv.org/abs/2208.05787v1
• Date: Thu, 11 Aug 2022 12:21:50 GMT
• Title: Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection
• Authors: Meiling Fang and Fadi Boutros and Naser Damer
• Abstract summary: We propose a completely unsupervised morphing attack detection solution via self-paced anomaly detection (SPL-MAD) We leverage the existing large-scale face recognition (FR) datasets and the unsupervised nature of convolutional autoencoders. Our experimental results show that the proposed SPL-MAD solution outperforms the overall performance of a wide range of supervised MAD solutions.
• Score: 8.981081097203088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The supervised-learning-based morphing attack detection (MAD) solutions achieve outstanding success in dealing with attacks from known morphing techniques and known data sources. However, given variations in the morphing attacks, the performance of supervised MAD solutions drops significantly due to the insufficient diversity and quantity of the existing MAD datasets. To address this concern, we propose a completely unsupervised MAD solution via self-paced anomaly detection (SPL-MAD) by leveraging the existing large-scale face recognition (FR) datasets and the unsupervised nature of convolutional autoencoders. Using general FR datasets that might contain unintentionally and unlabeled manipulated samples to train an autoencoder can lead to a diverse reconstruction behavior of attack and bona fide samples. We analyze this behavior empirically to provide a solid theoretical ground for designing our unsupervised MAD solution. This also results in proposing to integrate our adapted modified self-paced learning paradigm to enhance the reconstruction error separability between the bona fide and attack samples in a completely unsupervised manner. Our experimental results on a diverse set of MAD evaluation datasets show that the proposed unsupervised SPL-MAD solution outperforms the overall performance of a wide range of supervised MAD solutions and provides higher generalizability on unknown attacks.

Related papers

• Transferable Adversarial Attacks on SAM and Its Downstream Models [87.23908485521439]
  This paper explores the feasibility of adversarial attacking various downstream models fine-tuned from the segment anything model (SAM) To enhance the effectiveness of the adversarial attack towards models fine-tuned on unknown datasets, we propose a universal meta-initialization (UMI) algorithm.
  arXiv  Detail & Related papers  (2024-10-26T15:04:04Z)
• Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection [20.67964977754179]
  We investigate the potential of image representations for morphing attack detection (MAD) We develop supervised detectors by training a simple binary linear SVM on the extracted features and one-class detectors by modeling the distribution of bonafide features with a Gaussian Mixture Model (GMM) Our results indicate that attack-agnostic features can effectively detect morphing attacks, outperforming traditional supervised and one-class detectors from the literature in most scenarios.
  arXiv  Detail & Related papers  (2024-10-22T08:27:43Z)
• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• CUT: A Controllable, Universal, and Training-Free Visual Anomaly Generation Framework [11.609545429511595]
  We propose CUT: a Controllable, Universal and Training-free visual anomaly generation framework. We achieve controllable and realistic anomaly generation universally across both unseen data and novel anomaly types. By training the VLAD model with our generated anomalous samples, we achieve state-of-the-art performance on several benchmark anomaly detection tasks.
  arXiv  Detail & Related papers  (2024-06-03T07:58:09Z)
• DMAD: Dual Memory Bank for Real-World Anomaly Detection [90.97573828481832]
  We propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD) DMAD employs a dual memory bank to calculate feature distance and feature attention between normal and abnormal patterns. We evaluate DMAD on the MVTec-AD and VisA datasets.
  arXiv  Detail & Related papers  (2024-03-19T02:16:32Z)
• Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity [80.16488817177182]
  GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. We introduce three model stealing attacks to adapt to different actual scenarios.
  arXiv  Detail & Related papers  (2023-12-18T05:42:31Z)
• Diversity-Measurable Anomaly Detection [106.07413438216416]
  We propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity. PDM essentially decouples deformation from embedding and makes the final anomaly score more reliable.
  arXiv  Detail & Related papers  (2023-03-09T05:52:42Z)
• Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection [0.0]
  Empirical Risk Minimization (ERM) assumes that the performance of an algorithm on an unknown distribution can be approximated by averaging losses on the known training set. We propose a novel Diminishing Empirical Risk Minimization (DERM) framework to break through the limitations of ERM. DERM adaptively adjusts the impact of individual losses through a well-devised aggregation strategy.
  arXiv  Detail & Related papers  (2022-05-29T14:18:26Z)
• A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning [122.49765136434353]
  We present an effective method, called Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM), aiming to generate a sequence of adversarial examples. We also propose a new generative method called Contrastive Adversarial Training (CAT), which approaches equilibrium distribution of adversarial examples. Both quantitative and qualitative analysis on several natural image datasets and practical systems have confirmed the superiority of the proposed algorithm.
  arXiv  Detail & Related papers  (2020-10-15T16:07:26Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.