A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection

• URL: http://arxiv.org/abs/2510.14314v1
• Date: Thu, 16 Oct 2025 05:21:30 GMT
• Title: A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection
• Authors: Shivangi Yadav, Arun Ross,
• Abstract summary: An iris biometric system can be compromised by presentation attacks (PAs) where artifacts such as artificial eyes, printed eye images, or cosmetic contact lenses are presented to the system.<n>We introduce MID-StyleGAN, a new framework for generating synthetic ocular images that captures the PA and bonafide characteristics in multiple domains.<n>Experiments demonstrate that MID-StyleGAN outperforms existing methods in generating high-quality synthetic ocular images.
• Score: 6.369212768424994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An iris biometric system can be compromised by presentation attacks (PAs) where artifacts such as artificial eyes, printed eye images, or cosmetic contact lenses are presented to the system. To counteract this, several presentation attack detection (PAD) methods have been developed. However, there is a scarcity of datasets for training and evaluating iris PAD techniques due to the implicit difficulties in constructing and imaging PAs. To address this, we introduce the Multi-domain Image Translative Diffusion StyleGAN (MID-StyleGAN), a new framework for generating synthetic ocular images that captures the PA and bonafide characteristics in multiple domains such as bonafide, printed eyes and cosmetic contact lens. MID-StyleGAN combines the strengths of diffusion models and generative adversarial networks (GANs) to produce realistic and diverse synthetic data. Our approach utilizes a multi-domain architecture that enables the translation between bonafide ocular images and different PA domains. The model employs an adaptive loss function tailored for ocular data to maintain domain consistency. Extensive experiments demonstrate that MID-StyleGAN outperforms existing methods in generating high-quality synthetic ocular images. The generated data was used to significantly enhance the performance of PAD systems, providing a scalable solution to the data scarcity problem in iris and ocular biometrics. For example, on the LivDet2020 dataset, the true detect rate at 1% false detect rate improved from 93.41% to 98.72%, showcasing the impact of the proposed method.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach [0.0]
  Traditional deepfake detection methods fail against text-to-image systems such as Stable Diffusion and DALL-E.<n>This paper introduces a diffusion-based forensic framework that leverages multi-strength image reconstruction dynamics.
  arXiv  Detail & Related papers  (2025-11-01T01:35:54Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
  We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images. Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness. Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
  arXiv  Detail & Related papers  (2024-11-12T01:17:27Z)
• Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction [9.028901322902913]
  Metal artifacts in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. Deep learning-based approaches, particularly generative models, have been proposed for metal artifact reduction (MAR)
  arXiv  Detail & Related papers  (2024-08-14T02:37:26Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• Bi-LORA: A Vision-Language Approach for Synthetic Image Detection [14.448350657613364]
  Deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs) have ushered in an era of generating highly realistic images. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs) We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images.
  arXiv  Detail & Related papers  (2024-04-02T13:54:22Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [3.890243179348094]
  Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications.<n>This paper proposes a novel approach by developing controllable diffusion models for medical image synthesis, called DiffBoost.<n>We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis [4.724009208755395]
  We present ViT-DAE, which integrates vision transformers (ViT) and diffusion autoencoders for high-quality histopathology image synthesis. Our approach outperforms recent GAN-based and vanilla DAE methods in generating realistic images.
  arXiv  Detail & Related papers  (2023-04-03T15:00:06Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes [0.5076419064097732]
  We present an improved CycleGAN architecture that employs self-supervised discriminators to alleviate the need for numerous images. We also provide results obtained with small biological datasets on obvious and non-obvious cell phenotype variations.
  arXiv  Detail & Related papers  (2023-01-21T16:25:04Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.