Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality

• URL: http://arxiv.org/abs/2208.05864v2
• Date: Fri, 12 Aug 2022 07:15:35 GMT
• Title: Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality
• Authors: Biying Fu and Naser Damer
• Abstract summary: We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition. This work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores.
• Score: 6.889667606945215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique, but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. Our study looks intointra and inter-dataset detectability to evaluate the generalizability of such a detection concept on different morphing techniques and bona fide sources. Our final results point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct classification accuracy of over 70%.

Related papers

• Rank-based No-reference Quality Assessment for Face Swapping [88.53827937914038]
  The metric of measuring the quality in most face swapping methods relies on several distances between the manipulated images and the source image. We present a novel no-reference image quality assessment (NR-IQA) method specifically designed for face swapping.
  arXiv  Detail & Related papers  (2024-06-04T01:36:29Z)
• 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)
• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• 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)
• Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models [0.0]
  Morphed face images can be used to impersonate someone's identity for various malicious purposes. Existing MAD techniques rely on discriminative models that learn from examples of bona fide and morphed images. We propose a novel, diffusion-based MAD method that learns only from the characteristics of bona fide images.
  arXiv  Detail & Related papers  (2023-06-27T18:19:45Z)
• 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)
• 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)
• Surveillance Face Anti-spoofing [81.50018853811895]
  Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality. A large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
  arXiv  Detail & Related papers  (2023-01-03T07:09:57Z)
• PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection [6.24950085812444]
  A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Different methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document.
  arXiv  Detail & Related papers  (2021-08-23T17:04:51Z)
• Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks [104.8737334237993]
  We present comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For five popular image-to-image tasks, 16 deep models are analyzed from various standpoints. We show that unlike in image classification tasks, the performance degradation on image-to-image tasks can largely differ depending on various factors.
  arXiv  Detail & Related papers  (2021-04-30T14:20:33Z)
• On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection [12.936155415524937]
  Face Recognition Systems (FRS) are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image.
  arXiv  Detail & Related papers  (2020-07-06T12:32:41Z)

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.