Face Morphing: Fooling a Face Recognition System Is Simple!

• URL: http://arxiv.org/abs/2205.13796v1
• Date: Fri, 27 May 2022 07:17:09 GMT
• Title: Face Morphing: Fooling a Face Recognition System Is Simple!
• Authors: Stefan H\"ormann, Tianlin Kong, Torben Teepe, Fabian Herzog, Martin Knoche, Gerhard Rigoll
• Abstract summary: State-of-the-art face recognition approaches have shown remarkable results in predicting whether two faces belong to the same identity. However, the accuracy drops substantially when exposed to morphed faces, specifically generated to look similar to two identities. To generate morphed faces, we integrate a simple pretrained FR model into a generative adversarial network (GAN) and modify several loss functions for face morphing.
• Score: 4.4855664250147465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100% depending on the difficulty of the protocol. However, the accuracy drops substantially when exposed to morphed faces, specifically generated to look similar to two identities. To generate morphed faces, we integrate a simple pretrained FR model into a generative adversarial network (GAN) and modify several loss functions for face morphing. In contrast to previous works, our approach and analyses are not limited to pairs of frontal faces with the same ethnicity and gender. Our qualitative and quantitative results affirm that our approach achieves a seamless change between two faces even in unconstrained scenarios. Despite using features from a simpler FR model for face morphing, we demonstrate that even recent FR systems struggle to distinguish the morphed face from both identities obtaining an accuracy of only 55-70%. Besides, we provide further insights into how knowing the FR system makes it particularly vulnerable to face morphing attacks.

Related papers

• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration. We propose OSDFace, a novel one-step diffusion model for face restoration. Results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics.
  arXiv  Detail & Related papers  (2024-11-26T07:07:48Z)
• LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion [5.602947425285195]
  Face morphing attacks pose a severe security threat to face recognition systems. We present a representation-level face morphing approach, namely LADIMO, that performs morphing on two face recognition embeddings. We show that each face morph variant has an individual attack success rate, enabling us to maximize the morph attack potential.
  arXiv  Detail & Related papers  (2024-10-10T14:41:37Z)
• Quadruplet Loss For Improving the Robustness to Face Morphing Attacks [0.0]
  Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques. We introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks.
  arXiv  Detail & Related papers  (2024-02-22T16:10:39Z)
• Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection [4.349201388722244]
  Face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphing Attacks. We employ an pre-selection algorithm that pairs individuals based on face embedding similarity. We compare embeddings from two state-of-the-art face recognition systems in terms of their ability to detect Morphing Attacks.
  arXiv  Detail & Related papers  (2023-05-29T17:00:40Z)
• MorphGANFormer: Transformer-based Face Morphing and De-Morphing [55.211984079735196]
  StyleGAN-based approaches to face morphing are among the leading techniques. We propose a transformer-based alternative to face morphing and demonstrate its superiority to StyleGAN-based methods.
  arXiv  Detail & Related papers  (2023-02-18T19:09:11Z)
• Learning Fair Face Representation With Progressive Cross Transformer [79.73754444296213]
  We propose a progressive cross transformer (PCT) method for fair face recognition. We show that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance.
  arXiv  Detail & Related papers  (2021-08-11T01:31:14Z)
• Face Hallucination via Split-Attention in Split-Attention Network [58.30436379218425]
  convolutional neural networks (CNNs) have been widely employed to promote the face hallucination. We propose a novel external-internal split attention group (ESAG) to take into account the overall facial profile and fine texture details simultaneously. By fusing the features from these two paths, the consistency of facial structure and the fidelity of facial details are strengthened.
  arXiv  Detail & Related papers  (2020-10-22T10:09:31Z)
• MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN [22.220940043294334]
  We present a new approach for generating strong attacks using an Identity Prior Driven Generative Adversarial Network. The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System.
  arXiv  Detail & Related papers  (2020-09-03T15:08:38Z)
• Dual-discriminator GAN: A GAN way of profile face recognition [21.181356044588213]
  We propose a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN) In this paper, we proposed a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN)
  arXiv  Detail & Related papers  (2020-03-20T06:01:58Z)
• DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition [94.96686189033869]
  We propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) DotFAN can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity.
  arXiv  Detail & Related papers  (2020-02-23T08:16:34Z)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)

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.