DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing

• URL: http://arxiv.org/abs/2507.17158v1
• Date: Wed, 23 Jul 2025 02:43:49 GMT
• Title: DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing
• Authors: Bharath Krishnamurthy, Ajita Rattani,
• Abstract summary: Ocular biometrics in the visible spectrum have emerged as a prominent modality due to high accuracy, resistance to spoofing, and non-invasive nature.<n> morphing attacks, synthetic biometric traits created by blending features from multiple individuals, threaten biometric system integrity.
• Score: 3.9440964696313485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ocular biometrics in the visible spectrum have emerged as a prominent modality due to their high accuracy, resistance to spoofing, and non-invasive nature. However, morphing attacks, synthetic biometric traits created by blending features from multiple individuals, threaten biometric system integrity. While extensively studied for near-infrared iris and face biometrics, morphing in visible-spectrum ocular data remains underexplored. Simulating such attacks demands advanced generation models that handle uncontrolled conditions while preserving detailed ocular features like iris boundaries and periocular textures. To address this gap, we introduce DOOMGAN, that encompasses landmark-driven encoding of visible ocular anatomy, attention-guided generation for realistic morph synthesis, and dynamic weighting of multi-faceted losses for optimized convergence. DOOMGAN achieves over 20% higher attack success rates than baseline methods under stringent thresholds, along with 20% better elliptical iris structure generation and 30% improved gaze consistency. We also release the first comprehensive ocular morphing dataset to support further research in this domain.

Related papers

• ERGO: Excess-Risk-Guided Optimization for High-Fidelity Monocular 3D Gaussian Splatting [63.138778159026934]
  We propose an adaptive optimization framework guided by excess risk decomposition, termed ERGO.<n> ERGO dynamically estimates the view-specific excess risk and adaptively adjust loss weights during optimization.<n>Experiments on the Google Scanned Objects dataset and the OmniObject3D dataset demonstrate the superiority of ERGO over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-10T20:44:43Z)
• DNA: Uncovering Universal Latent Forgery Knowledge [39.19379714306656]
  forgery detection capability is already encoded within pre-trained models.<n>DNA framework employs a coarse-to-fine excavation mechanism.<n>Hifi-Gen is a high-fidelity synthetic benchmark built upon the very latest models.
  arXiv  Detail & Related papers  (2026-01-30T03:48:30Z)
• 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)
• A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection [6.369212768424994]
  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.
  arXiv  Detail & Related papers  (2025-10-16T05:21:30Z)
• Learning Protein-Ligand Binding in Hyperbolic Space [21.421085367060343]
  HypSeek is a hyperbolic representation learning framework that embeds protein pockets and sequences into Lorentz-model hyperbolic space.<n>By leveraging the exponential geometry and negative curvature of hyperbolic space, HypSeek enables expressive, affinity-sensitive embeddings.<n>Our mode unifies virtual screening and affinity ranking in a single framework, introducing a protein-guided three-tower architecture.
  arXiv  Detail & Related papers  (2025-08-21T11:56:25Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images [1.1084686909647639]
  We propose a self-supervised monocular depth estimation network tailored for endoscopic scenes.<n>Existing methods, though accurate, typically assume consistent illumination.<n>These variations lead to incorrect geometric interpretations and unreliable self-supervised signals.
  arXiv  Detail & Related papers  (2025-04-24T14:12:57Z)
• CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion [8.311453061101899]
  We introduce CAFusion, a novel approach for synthesizing perirectal lymph nodes.<n>By leveraging Signed Distance Functions (SDF), CAFusion generates highly realistic 3D anatomical structures.<n> Experimental results demonstrate that our synthetic data substantially improve segmentation performance.
  arXiv  Detail & Related papers  (2025-03-10T04:59:54Z)
• Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized Myopia Management [17.197828632845198]
  Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss.<n>Recent understanding of eye-shape-based biomarkers requires magnetic resonance imaging (MRI)<n>Fundus2Globe is the first AI framework that synthesizes patient-specific 3D eye globes from 2D color fundus photographs.
  arXiv  Detail & Related papers  (2025-02-18T10:02:39Z)
• Cross-Spectral Vision Transformer for Biometric Authentication using Forehead Subcutaneous Vein Pattern and Periocular Pattern [1.2137050542976475]
  This paper proposes a novel lightweight cross-spectral vision transformer (CS-ViT) for biometric authentication.<n>The framework comprises a cross-spectral dual-channel architecture designed to handle two distinct biometric traits.<n>The proposed algorithm was rigorously evaluated using the Forehead Subcutaneous Vein Pattern and Periocular Biometric Pattern database.
  arXiv  Detail & Related papers  (2024-12-26T10:40:15Z)
• Periocular biometrics: databases, algorithms and directions [69.35569554213679]
  Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. This paper presents a review of the state of the art in periocular biometric research.
  arXiv  Detail & Related papers  (2023-07-26T11:14:36Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Periocular Biometrics: A Modality for Unconstrained Scenarios [66.93179447621188]
  Periocular biometrics includes the externally visible region of the face that surrounds the eye socket. The COVID-19 pandemic has highlighted its importance, as the ocular region remained the only visible facial area even in controlled settings.
  arXiv  Detail & Related papers  (2022-12-28T12:08:27Z)
• Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues [81.15465149555864]
  We establish the first joint face spoofing and detection benchmark using both visual appearance and physiological r cues. To enhance the r periodicity discrimination, we design a two-branch physiological network using both facial powerful rtemporal signal map and its continuous wavelet transformed counterpart as inputs.
  arXiv  Detail & Related papers  (2022-08-10T15:41:48Z)
• Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition [24.267703297385413]
  This paper presents a new deep periocular recognition framework called attribute-based deep periocular recognition (ADPR) ADPR predicts soft biometrics and incorporates the prediction into a periocular recognition algorithm to determine identity from periocular images with high accuracy. Experimental results indicate that our soft biometric based periocular recognition approach outperforms other state-of-the-art methods for periocular recognition in wild environments.
  arXiv  Detail & Related papers  (2021-11-02T01:51:37Z)
• Heterogeneous Face Frontalization via Domain Agnostic Learning [74.86585699909459]
  We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis.
  arXiv  Detail & Related papers  (2021-07-17T20:41:41Z)

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.