Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications

• URL: http://arxiv.org/abs/2408.09558v1
• Date: Sun, 18 Aug 2024 17:53:26 GMT
• Title: Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications
• Authors: Juan E. Tapia, Maximilian Russo, Christoph Busch,
• Abstract summary: One of the main scenarios is printing morphed images and submitting the respective print in a passport application process. Small datasets are available to train the MAD algorithm because of privacy concerns. This paper proposes two different methods based on transfer-transfer for automatically creating digital print/scan face images.
• Score: 7.287930923353593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Morphing Attack Detection (MAD) is a relevant topic that aims to detect attempts by unauthorised individuals to access a "valid" identity. One of the main scenarios is printing morphed images and submitting the respective print in a passport application process. Today, small datasets are available to train the MAD algorithm because of privacy concerns and the limitations resulting from the effort associated with the printing and scanning of images at large numbers. In order to improve the detection capabilities and spot such morphing attacks, it will be necessary to have a larger and more realistic dataset representing the passport application scenario with the diversity of devices and the resulting printed scanned or compressed images. Creating training data representing the diversity of attacks is a very demanding task because the training material is developed manually. This paper proposes two different methods based on transfer-transfer for automatically creating digital print/scan face images and using such images in the training of a Morphing Attack Detection algorithm. Our proposed method can reach an Equal Error Rate (EER) of 3.84% and 1.92% on the FRGC/FERET database when including our synthetic and texture-transfer print/scan with 600 dpi to handcrafted images, respectively.

Related papers

• 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)
• PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding [102.07914175196817]
  PhotoMaker is an efficient personalized text-to-image generation method. It encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information.
  arXiv  Detail & Related papers  (2023-12-07T17:32:29Z)
• DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models [79.71665540122498]
  We propose a method for detecting unauthorized data usage by planting the injected content into the protected dataset. Specifically, we modify the protected images by adding unique contents on these images using stealthy image warping functions. By analyzing whether the model has memorized the injected content, we can detect models that had illegally utilized the unauthorized data.
  arXiv  Detail & Related papers  (2023-07-06T16:27:39Z)
• Human-imperceptible, Machine-recognizable Images [76.01951148048603]
  A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data. This paper proposes an efficient privacy-preserving learning paradigm, where images are encrypted to become human-imperceptible, machine-recognizable'' We show that the proposed paradigm can ensure the encrypted images have become human-imperceptible while preserving machine-recognizable information.
  arXiv  Detail & Related papers  (2023-06-06T13:41:37Z)
• Synthetic ID Card Image Generation for Improving Presentation Attack Detection [12.232059909207578]
  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.
  arXiv  Detail & Related papers  (2022-10-31T19:07:30Z)
• 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)
• Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform [58.60004238261117]
  We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815) Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. The proposed framework allows us to perform task-aware image compressions for various tasks.
  arXiv  Detail & Related papers  (2021-08-21T17:30:06Z)
• M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
  forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information. In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection. We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
  arXiv  Detail & Related papers  (2021-04-20T05:43:44Z)
• 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)
• Printing and Scanning Attack for Image Counter Forensics [11.193867567895353]
  Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks. We explore another method of highly plausible attack: printing and scanning.
  arXiv  Detail & Related papers  (2020-04-27T00:32:15Z)
• A Novel Inspection System For Variable Data Printing Using Deep Learning [0.9176056742068814]
  We present a novel approach for inspecting variable data prints (VDP) with an ultra-low false alarm rate (0.005%) The system is based on a comparison between two images: a reference image and an image captured by low-cost scanners.
  arXiv  Detail & Related papers  (2020-01-13T15:07:13Z)

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.