Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

• URL: http://arxiv.org/abs/2007.11469v1
• Date: Wed, 22 Jul 2020 14:41:14 GMT
• Title: Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
• Authors: Guillaume Heusch and Anjith George and David Geissbuhler and Zohreh Mostaani and Sebastien Marcel
• Abstract summary: Face presentation attack detection is performed using recent models based on Convolutional Neural Networks. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks.
• Score: 6.684752451476642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community.

Related papers

• Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector [97.92369017531038]
  We build a new laRge-scale Adervsarial images dataset with Diverse hArmful Responses (RADAR) We then develop a novel iN-time Embedding-based AdveRSarial Image DEtection (NEARSIDE) method, which exploits a single vector that distilled from the hidden states of Visual Language Models (VLMs) to achieve the detection of adversarial images against benign ones in the input.
  arXiv  Detail & Related papers  (2024-10-30T10:33:10Z)
• SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models [63.946809247201905]
  We introduce a new benchmark, namely SHIELD, to evaluate the ability of MLLMs on face spoofing and forgery detection. We design true/false and multiple-choice questions to evaluate multimodal face data in these two face security tasks. The results indicate that MLLMs hold substantial potential in the face security domain.
  arXiv  Detail & Related papers  (2024-02-06T17:31:36Z)
• Cross-Modality Perturbation Synergy Attack for Person Re-identification [66.48494594909123]
  The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. Existing attack methods have primarily focused on the characteristics of the visible image modality. This study proposes a universal perturbation attack specifically designed for cross-modality ReID.
  arXiv  Detail & Related papers  (2024-01-18T15:56:23Z)
• Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study [7.681417534211941]
  This paper presents a multispectral framework for differential morphing-attack detection (D-MAD) The proposed multispectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks.
  arXiv  Detail & Related papers  (2023-04-07T07:03:00Z)
• Shuffled Patch-Wise Supervision for Presentation Attack Detection [12.031796234206135]
  Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most presentation attack detection systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. We propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN.
  arXiv  Detail & Related papers  (2021-09-08T08:14:13Z)
• Robust Data Hiding Using Inverse Gradient Attention [82.73143630466629]
  In the data hiding task, each pixel of cover images should be treated differently since they have divergent tolerabilities. We propose a novel deep data hiding scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets.
  arXiv  Detail & Related papers  (2020-11-21T19:08:23Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)
• Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset [9.783887684870654]
  Fingerprint presentation attack detection is becoming an increasingly challenging problem. We study the usefulness of multiple recently introduced sensing modalities. We conducted a comprehensive analysis using a fully convolutional deep neural network framework.
  arXiv  Detail & Related papers  (2020-06-12T22:38:23Z)
• 3D Face Anti-spoofing with Factorized Bilinear Coding [35.30886962572515]
  We propose a novel anti-spoofing method from the perspective of fine-grained classification. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection.
  arXiv  Detail & Related papers  (2020-05-12T03:09:20Z)
• Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study [0.5801044612920815]
  Cross-dataset evaluation on face PAD showed better generalization than state of the art. We propose the use of a single deep network trained to detect both face and iris attacks.
  arXiv  Detail & Related papers  (2020-04-25T02:06:19Z)

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.