Synthetic ID Card Image Generation for Improving Presentation Attack
Detection
- URL: http://arxiv.org/abs/2211.00098v1
- Date: Mon, 31 Oct 2022 19:07:30 GMT
- Title: Synthetic ID Card Image Generation for Improving Presentation Attack
Detection
- Authors: Daniel Benalcazar, Juan E. Tapia, Sebastian Gonzalez, and Christoph
Busch
- Abstract summary: This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks.
Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.
- Score: 12.232059909207578
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Currently, it is ever more common to access online services for activities
which formerly required physical attendance. From banking operations to visa
applications, a significant number of processes have been digitised, especially
since the advent of the COVID-19 pandemic, requiring remote biometric
authentication of the user. On the downside, some subjects intend to interfere
with the normal operation of remote systems for personal profit by using fake
identity documents, such as passports and ID cards. Deep learning solutions to
detect such frauds have been presented in the literature. However, due to
privacy concerns and the sensitive nature of personal identity documents,
developing a dataset with the necessary number of examples for training deep
neural networks is challenging. This work explores three methods for
synthetically generating ID card images to increase the amount of data while
training fraud-detection networks. These methods include computer vision
algorithms and Generative Adversarial Networks. Our results indicate that
databases can be supplemented with synthetic images without any loss in
performance for the print/scan Presentation Attack Instrument Species (PAIS)
and a loss in performance of 1% for the screen capture PAIS.
Related papers
- Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation [56.46932751058042]
We train a learnable prompt prefix for text-to-image diffusion models, which forces the model to generate anonymized facial identities.
Experiments demonstrate the successful anonymization performance of APL, which anonymizes any specific individuals without compromising the quality of non-identity-specific image generation.
arXiv Detail & Related papers (2024-05-27T07:38:26Z) - Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - Open-Set: ID Card Presentation Attack Detection using Neural Transfer
Style [2.946386240942919]
This work explores ID card Presentation Attack Instruments (PAI) in order to improve the generation of samples with four Generative Adversarial Networks (GANs) based image translation models.
We obtain an EER performance increase of 0.63% points for print attacks and a loss of 0.29% for screen capture attacks.
arXiv Detail & Related papers (2023-12-21T16:28:08Z) - 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) - Presentation Attack Detection using Convolutional Neural Networks and
Local Binary Patterns [7.946115381584211]
Presentation attacks are a serious threat because they do not require significant time, expense, or skill to carry out.
This research compares three different software-based methods for facial and iris presentation attack detection in images.
arXiv Detail & Related papers (2023-11-23T20:57:07Z) - An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face
Detection [1.2599533416395763]
We present a novel visual explanation approach named Ensemble XAI to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-Grad1)
The experiments have been performed on three publicly available datasets namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack (WMCA) and Makeup Induced Face Spoofing (MIFS)
arXiv Detail & Related papers (2023-04-23T13:43:06Z) - Face Presentation Attack Detection [59.05779913403134]
Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment.
However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
arXiv Detail & Related papers (2022-12-07T14:51:17Z) - Differential Anomaly Detection for Facial Images [15.54185745912878]
Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation.
Most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time.
We introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images.
arXiv Detail & Related papers (2021-10-07T13:45:13Z) - Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for
Age and Gender Prediction on Mobile Ocular Images [53.913598771836924]
We address the use of selfie ocular images captured with smartphones to estimate age and gender.
We adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge.
Some networks are further pre-trained for face recognition, for which very large training databases are available.
arXiv Detail & Related papers (2021-03-31T01:48:29Z) - Generalized Iris Presentation Attack Detection Algorithm under
Cross-Database Settings [63.90855798947425]
Presentation attacks pose major challenges to most of the biometric modalities.
We propose a generalized deep learning-based presentation attack detection network, MVANet.
It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
arXiv Detail & Related papers (2020-10-25T22:42:27Z) - 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)
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.