Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study

• URL: http://arxiv.org/abs/2605.14845v1
• Date: Thu, 14 May 2026 13:53:28 GMT
• Title: Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study
• Abstract summary: This work presents an exploratory study evaluating the zero-shot performance of state-of-the-art Vision-Language Models (VLMs)<n>We introduce a scoring protocol that extracts latent token probabilities to compute robust biometric scores.<n>In random forgery scenarios, the zero-shot VLM achieves exceptional discrimination, with GPT-5.2 reaching an Equal Error Rate of 0.32% in mobile tasks.<n>In skilled forgery scenarios, where the task is more challenging because both signatures are almost identical, the results are significantly worse, and a critical "Rationalization Trap" emerges.
• Score: 9.575629835755972
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in Vision-Language Models (VLMs) have demonstrated strong capabilities in general visual reasoning, yet their applicability to rigorous biometric tasks remains unexplored. This work presents an exploratory study evaluating the zero-shot performance of state-of-the-art VLMs (GPT-5.2 and Gemini 2.5 Pro) on the Signature Verification Challenge (SVC) benchmark. To enable visual processing, raw kinematic time-series are converted into static images, encoding pressure information into stroke opacity whenever available in the source data. Furthermore, we introduce a scoring protocol that extracts latent token probabilities to compute robust biometric scores. Experimental results reveal a significant performance dichotomy dependent on signal quality and forgery type. In random forgery scenarios, the zero-shot VLM achieves exceptional discrimination, with GPT-5.2 reaching an Equal Error Rate of 0.32% in mobile tasks, outperforming supervised state-of-the-art systems. Conversely, in skilled forgery scenarios, where the task is more challenging because both signatures are almost identical, the results are significantly worse, and a critical "Rationalization Trap" emerges: chain-of-thought (CoT) reasoning degrades performance as the model produces kinematic hallucinations to justify forgery artifacts as natural variability.

Related papers

• Employing Vision-Language Models for Face Image Quality Assessment [0.023141219541346198]
  Face Image Quality Assessment (FIQA) is a crucial control step in biometric pipelines.<n>State-of-the-art FIQA methods achieve high utility but typically operate as "black boxes"<n>We investigate the potential of off-the-shelf Vision-Language Models (VLMs) to bridge this gap by performing FIQA in a zero-shot setting.
  arXiv  Detail & Related papers  (2026-05-17T14:57:52Z)
• Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models [8.630726904040781]
  Explicit Visual Premise Verification (EVPV) is a lightweight verification interface that conditions step scoring on the reliability of the visual premises a step depends on.<n>EVPV improves step-level verification and consistently boosts Best-of-N reranking accuracy over strong baselines.
  arXiv  Detail & Related papers  (2026-03-17T08:40:26Z)
• RobustVisRAG: Causality-Aware Vision-Based Retrieval-Augmented Generation under Visual Degradations [12.753436440584409]
  Vision-based Retrieval-Augmented Generation (VisRAG) leverages vision-language models (VLMs) to jointly retrieve relevant visual documents and generate grounded answers based on multimodal evidence.<n>Existing VisRAG models degrade in performance when visual inputs suffer from distortions such as blur, noise, low light, or shadow, where semantic and degradation factors become entangled within pretrained visual encoders.<n>We introduce RobustVisRAG, a causality-guided dual-path framework that improves VisRAG robustness while preserving efficiency and zero-shot generalization.
  arXiv  Detail & Related papers  (2026-02-25T15:27:57Z)
• ReViP: Reducing False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance [50.05984919728878]
  We present ReViP, a novel VLA framework with Vision-Proprioception Rebalance to enhance visual grounding and robustness under perturbations.<n>Specifically, we use an external VLM as a task-stage observer to extract real-time task-centric visual cues from visual observations.<n>To evaluate false completion, we propose the first False-Completion Benchmark Suite built on LIBERO with controlled settings such as Object-Drop.
  arXiv  Detail & Related papers  (2026-01-23T11:31:07Z)
• Lost in the Noise: How Reasoning Models Fail with Contextual Distractors [57.31788955167306]
  Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information.<n>We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks.<n>Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors.
  arXiv  Detail & Related papers  (2026-01-12T05:43:51Z)
• VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics [0.0]
  Recent advances in AI-driven image generation have introduced new challenges for verifying the authenticity of digital evidence in forensic investigations.<n>Modern generative models can produce visually consistent forgeries that evade traditional detectors based on pixel or compression artefacts.<n>This paper introduces Vision-Attention Anomaly Scoring (VAAS), a novel dual-module framework that integrates global attention-based anomaly estimation.
  arXiv  Detail & Related papers  (2025-12-17T15:05:40Z)
• Task-Model Alignment: A Simple Path to Generalizable AI-Generated Image Detection [57.17054616831796]
  Vision Language Models (VLMs) are increasingly adopted for AI-generated images (AIGI) detection.<n>VLMs' underperformance is attributed to task-model misalignment.<n>In this paper, we formalize AIGI detection as two complementary tasks--semantic consistency checking and pixel-artifact detection.
  arXiv  Detail & Related papers  (2025-12-07T09:19:00Z)
• VOGUE: Guiding Exploration with Visual Uncertainty Improves Multimodal Reasoning [62.09195763860549]
  Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models (LLMs) but struggles with exploration.<n>We introduce $textbfVOGUE (Visual Uncertainty Guided Exploration)$, a novel method that shifts exploration from the output (text) to the input (visual) space.<n>Our work shows that grounding exploration in the inherent uncertainty of visual inputs is an effective strategy for improving multimodal reasoning.
  arXiv  Detail & Related papers  (2025-10-01T20:32:08Z)
• 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)
• Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection [64.65563422852568]
  We improve the challenging monocular 3D object detection problem with a general semi-supervised framework. We introduce a novel, simple, yet effective Augment and Criticize' framework that explores abundant informative samples from unlabeled data. The two new detectors, dubbed 3DSeMo_DLE and 3DSeMo_FLEX, achieve state-of-the-art results with remarkable improvements for over 3.5% AP_3D/BEV (Easy) on KITTI.
  arXiv  Detail & Related papers  (2023-03-20T16:28:15Z)

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.