Face Morphing Attack Detection Using Privacy-Aware Training Data

• URL: http://arxiv.org/abs/2207.00899v1
• Date: Sat, 2 Jul 2022 19:00:48 GMT
• Title: Face Morphing Attack Detection Using Privacy-Aware Training Data
• Authors: Marija Ivanovska, Andrej Kronov\v{s}ek, Peter Peer, Vitomir \v{S}truc, Borut Batagelj
• Abstract summary: Images of morphed faces pose a serious threat to face recognition--based security systems. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data.
• Score: 0.991629944808926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.

Related papers

• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• Hierarchical Generative Network for Face Morphing Attacks [7.34597796509503]
  Face morphing attacks circumvent face recognition systems (FRSs) by creating a morphed image that contains multiple identities. We propose a novel morphing attack method to improve the quality of morphed images and better preserve the contributing identities.
  arXiv  Detail & Related papers  (2024-03-17T06:09:27Z)
• Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations [32.33331065408444]
  Existing deepfake detectors are not optimized for this detection task when an image is associated with a specific and identifiable individual. This study focuses on the deepfake detection of facial images of individual public figures. We demonstrate that the detection performance can be improved by exploiting the idempotency property of neural networks.
  arXiv  Detail & Related papers  (2023-12-13T10:21:00Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• Fused Classification For Differential Face Morphing Detection [0.0]
  Face morphing, a presentation attack technique, poses significant security risks to face recognition systems. Traditional methods struggle to detect morphing attacks, which involve blending multiple face images. We propose an extended approach based on fused classification method for no-reference scenario.
  arXiv  Detail & Related papers  (2023-09-01T16:14:29Z)
• Face Feature Visualisation of Single Morphing Attack Detection [13.680968065638108]
  This paper proposes an explainable visualisation of different face feature extraction algorithms. It enables the detection of bona fide and morphing images for single morphing attack detection. The visualisation may help to develop a Graphical User Interface for border policies.
  arXiv  Detail & Related papers  (2023-04-25T17:51:23Z)
• Attribute-preserving Face Dataset Anonymization via Latent Code Optimization [64.4569739006591]
  We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN. We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
  arXiv  Detail & Related papers  (2023-03-20T17:34:05Z)
• MorDeephy: Face Morphing Detection Via Fused Classification [0.0]
  We introduce a novel deep learning strategy for a single image face morphing detection. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalising the task of morphing detection to unseen scenarios.
  arXiv  Detail & Related papers  (2022-08-05T11:39:22Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)

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.