In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems | Paper Citation Record

Observation 3bbc4670-ffed-44fd-8264-43edf3d73188 · outbound

This paper cites Information Tech- nology - Biometric presentation attack detection - Part 3: Testing and Reporting, International Organization for Standardization, 2023.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Information Tech- nology - Biometric presentation attack detection - Part 3: Testing and Reporting, International Organization for Standardization, 2023

Reference 1

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Observation 17759730-c01c-41ce-8c0d-00f2b440fcb2 · outbound

This paper cites On the generalisation capabilities of fisher vector-based face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems On the generalisation capabilities of fisher vector-based face presentation attack detection,

Reference 2

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Observation 288eca7c-0e8c-412e-84ad-de695f470397 · outbound

This paper cites MADation: Face Morphing Attack Detection with Foundation Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems MADation: Face Morphing Attack Detection with Foundation Models

Reference 3

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Observation b0ece18f-ff03-4cd8-a497-492c0e87d9b3 · outbound

This paper cites FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:10.801542Z

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Observation 1e779dc7-e67d-466b-a7b0-912ccd56bd2c · outbound

This paper cites Vision-Language Models for Vision Tasks: A Survey.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Vision-Language Models for Vision Tasks: A Survey

Reference 5

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Observation bfa899c5-5773-4e40-8164-8f416b58c062 · outbound

This paper cites Shield: An evaluation benchmark for face spoofing and forgery detection with multimodal large language models,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Shield: An evaluation benchmark for face spoofing and forgery detection with multimodal large language models,

Reference 6

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Observation 032bae89-b6d6-4ce0-ab77-df6ef61e82c9 · outbound

This paper cites Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:43:17.731412Z

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Observation f2dccfc5-2e83-4144-a929-ae63588b1f9c · outbound

This paper cites ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:43:17.516719Z

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Observation 3c019798-b609-4faa-93e1-bcef45edc447 · outbound

This paper cites Information Technology – Methodologies to evaluate the resistance of biometric recognition systems to morphing attacks , International Organization for Standardization, 2025.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Information Technology – Methodologies to evaluate the resistance of biometric recognition systems to morphing attacks , International Organization for Standardization, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.135116Z

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Observation 0edd10be-2ded-4c96-a6a0-8a181a8accdb · outbound

This paper cites Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.220997Z

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Observation 55a02f97-0409-43d4-8718-dfc6102c34a8 · outbound

This paper cites Fisher vector encoding of dense-bsif features for unknown face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Fisher vector encoding of dense-bsif features for unknown face presentation attack detection,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.287342Z

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Observation 11ab1a0c-1c01-4eff-a0c7-d30661b3acde · outbound

This paper cites On the generalisation capabilities of fisher vector based face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems On the generalisation capabilities of fisher vector based face presentation attack detection,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.386040Z

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Unavailable: canonical work link unavailable.

Observation f0cd404e-322b-4fd5-b6b5-b6d2b4deaa61 · outbound

This paper cites Fusion of multi-scale local phase quantization features for face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Fusion of multi-scale local phase quantization features for face presentation attack detection,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.459538Z

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Observation a2b709d1-a167-4e89-ad69-b3247c4d0f9c · outbound

This paper cites Patchswap: Boosting the generalizability of face presentation attack detection by identity-aware patch swapping,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Patchswap: Boosting the generalizability of face presentation attack detection by identity-aware patch swapping,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.554885Z

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Observation b7bdb007-60ec-4931-becb-9402fdc58135 · outbound

This paper cites Face presentation attack detection by exca- vating causal clues and adapting embedding statistics,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face presentation attack detection by exca- vating causal clues and adapting embedding statistics,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.595351Z

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Observation 87cef141-ff76-4923-a10e-89162cb56dbd · outbound

This paper cites Deep pixel-wise binary supervision for face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Deep pixel-wise binary supervision for face presentation attack detection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.690994Z

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Observation 42d0b106-2c73-42a9-ac2b-fb97ef1b8f6b · outbound

This paper cites On the effectiveness of vision transformers for zero-shot face anti-spoofing,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems On the effectiveness of vision transformers for zero-shot face anti-spoofing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:32.233094Z

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Observation 84a7609f-cb74-43df-a615-5c7a0772f75c · outbound

This paper cites Are foundation models all you need for zero-shot face presentation attack detection?.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Are foundation models all you need for zero-shot face presentation attack detection?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:32.061195Z

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Observation 314bb2ea-6ff1-40e3-b26e-352991953f9a · outbound

This paper cites Learn Convolutional Neural Network for Face Anti-Spoofing.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Learn Convolutional Neural Network for Face Anti-Spoofing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:11.909524Z

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Observation 603b5981-5b6e-410f-9aaa-18c4a01ced80 · outbound

This paper cites Caffe: Convolutional architecture for fast feature embedding,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Caffe: Convolutional architecture for fast feature embedding,

Reference 20

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Observation 4dd2e6ce-3819-4982-80ce-a08d39c30494 · outbound

This paper cites Deep face recognition,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Deep face recognition,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:31.673199Z

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Observation ed0a88a5-8008-49cb-8002-fcf00c62d00a · outbound

This paper cites Learning temporal features using LSTM- CNN architecture for face anti-spoofing,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Learning temporal features using LSTM- CNN architecture for face anti-spoofing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:31.475980Z

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Observation ab739aca-c530-414e-b1c2-e033d0978ab6 · outbound

This paper cites Mixnet for generalized face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Mixnet for generalized face presentation attack detection,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:31.241630Z

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Observation c33e7ce0-9190-4b6e-b0d1-2a620e0a189c · outbound

This paper cites Learnable multi- level frequency decomposition and hierarchical attention mechanism for generalized face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Learnable multi- level frequency decomposition and hierarchical attention mechanism for generalized face presentation attack detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:31.012795Z

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Observation 5eb12cd5-978e-4b48-947e-672155274d42 · outbound

This paper cites A dual-stream framework for 3d mask face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems A dual-stream framework for 3d mask face presentation attack detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:30.760520Z

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Observation ebda8852-1131-4b79-a244-7e460096a3f4 · outbound

This paper cites Contrastive context-aware learning for 3d high-fidelity mask face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Contrastive context-aware learning for 3d high-fidelity mask face presentation attack detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:30.487874Z

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Observation f79bcfe8-179d-4d6e-9379-3365ace60d47 · outbound

This paper cites One-class knowledge distillation for face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems One-class knowledge distillation for face presentation attack detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:30.198170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c405e46-fb77-4f15-bb94-4f7cbc7d1eec · outbound

This paper cites Learning how to recognize faces in heterogeneous environments,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Learning how to recognize faces in heterogeneous environments,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:29.928638Z

Source-reported events for the cited work

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Observation e4f879ce-0a74-4bff-8009-c3f5dc254807 · outbound

This paper cites Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:29.641582Z

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Observation 5fe699e5-1eec-44ff-ad49-1a1de72ce44a · outbound

This paper cites Multi-domain incremental learning for face presentation attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Multi-domain incremental learning for face presentation attack detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:29.410529Z

Source-reported events for the cited work

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Observation 6e957736-8f69-4e7f-bc02-5784c4b99dcf · outbound

This paper cites Person-specific face antispoofing with subject domain adaptation,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Person-specific face antispoofing with subject domain adaptation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:29.190605Z

Source-reported events for the cited work

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Observation 8ebe8da2-7951-428b-b633-21727fb3f897 · outbound

This paper cites A face antispoofing database with diverse attacks,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems A face antispoofing database with diverse attacks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:28.812823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 587ffa4f-4626-402f-bbc0-b64f99cd0845 · outbound

This paper cites On the effectiveness of local binary patterns in face anti-spoofing,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems On the effectiveness of local binary patterns in face anti-spoofing,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:28.482500Z

Source-reported events for the cited work

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Observation ead5feee-983f-4737-817f-e6c2f4548f48 · outbound

This paper cites Oulu-npu: A mobile face presentation attack database with real-world variations,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Oulu-npu: A mobile face presentation attack database with real-world variations,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:28.210701Z

Source-reported events for the cited work

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Observation c5bc817f-d399-4be6-a889-42bd98e18602 · outbound

This paper cites Face spoof detection with image distortion analysis,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face spoof detection with image distortion analysis,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:27.890392Z

Source-reported events for the cited work

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Observation b833795d-87be-4afb-b0b2-d3cb2df934ea · outbound

This paper cites The FERET database and evaluation procedure for face-recognition algorithms,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems The FERET database and evaluation procedure for face-recognition algorithms,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:27.542976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3f81785e-af0e-4ba3-bf8f-edb832db4b13 · outbound

This paper cites Overview of the face recognition grand challenge,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Overview of the face recognition grand challenge,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:27.208912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0d30e7b-9bd4-4123-901f-4a2f97ad9db6 · outbound

This paper cites Face analysis technology evaluation (fate) part 4: Morph - performance of automated face morph detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face analysis technology evaluation (fate) part 4: Morph - performance of automated face morph detection,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:26.864942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d816ec4c-6c7a-41ec-a650-76d9389af61d · outbound

This paper cites 67-78, 2019, accessed: 2025-01-21.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems 67-78, 2019, accessed: 2025-01-21

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:26.612532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8826db8d-8e35-4f78-83f3-e3d227c2aa54 · outbound

This paper cites Morph deterction from single face image: A multi-algorithm fusion approach,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Morph deterction from single face image: A multi-algorithm fusion approach,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:26.240601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 90c7cf23-bd43-4569-8290-ee3ed2271261 · outbound

This paper cites Morphing attack detection-database, evaluation platform, and bench- marking,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Morphing attack detection-database, evaluation platform, and bench- marking,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:25.878107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3228d96-8f9d-4482-a64f-5c11cbc9b664 · outbound

This paper cites A principal component analysis-based approach for single morphing attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems A principal component analysis-based approach for single morphing attack detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:25.743607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation deae58c9-c6e6-4bb6-a672-d100570284cf · outbound

This paper cites PRNU variance analysis for morphed face image detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems PRNU variance analysis for morphed face image detection,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:25.560311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0c657f8-e778-494d-8c03-d2d66bee2a47 · outbound

This paper cites Detection of face morphing attacks based on PRNU analysis,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Detection of face morphing attacks based on PRNU analysis,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:25.407595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d8894f42-4217-4159-81b1-4e62c7d12161 · outbound

This paper cites Morphed face detection based on deep color residual noise,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Morphed face detection based on deep color residual noise,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:25.066743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 13ffd031-3d03-4b0f-9a3c-3d5ef55d13c3 · outbound

This paper cites Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:24.700567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 20c3509f-4d74-4c43-98b6-d5f6b989aa8e · outbound

This paper cites Generalized single- image-based morphing attack detection using deep representations from vision transformer,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Generalized single- image-based morphing attack detection using deep representations from vision transformer,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:24.414140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da035eda-5709-4067-bd55-39c445585a6c · outbound

This paper cites Single-morphing attack detection using few-shot learning and triplet-loss,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Single-morphing attack detection using few-shot learning and triplet-loss,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:24.035462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8352205-26eb-462e-b7bd-4f770bc5c9e7 · outbound

This paper cites Detection of face morphing attacks by deep learning,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Detection of face morphing attacks by deep learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:23.768734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1a06e67-ff3f-4bcb-810a-1a111d5b318e · outbound

This paper cites Face morphing attack generation and detection: A comprehensive survey,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face morphing attack generation and detection: A comprehensive survey,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:23.520208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 796f71f1-f986-454c-b36b-74aa83b70c96 · outbound

This paper cites Towards making morphing attack detection robust using hybrid scale-space colour texture features,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Towards making morphing attack detection robust using hybrid scale-space colour texture features,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:23.222974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation abf0af5f-f932-4824-a7c0-78b00c01c0a3 · outbound

This paper cites Robust morph- detection at automated border control gate using deep decomposed 3D shape & diffuse reflectance,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Robust morph- detection at automated border control gate using deep decomposed 3D shape & diffuse reflectance,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:23.024343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 507b5974-0075-4beb-84cc-2a7897ca978b · outbound

This paper cites Detecting morphed face images using facial landmarks,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Detecting morphed face images using facial landmarks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:22.818045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0711408f-f944-4701-a9e7-3ddbec7f8e0a · outbound

This paper cites Detecting face morphing attacks by analyzing the directed distances of facial landmarks shifts,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Detecting face morphing attacks by analyzing the directed distances of facial landmarks shifts,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:22.630158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1d6cfcf-872f-4813-9172-4bac14df8ea1 · outbound

This paper cites Multispectral imaging for differential face morphing attack detection: A preliminary study,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Multispectral imaging for differential face morphing attack detection: A preliminary study,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:22.439239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7eb5196e-7183-4df2-a37c-f989829dde49 · outbound

This paper cites A multi-detector solution towards an accurate and generalized detection of face morphing attacks,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems A multi-detector solution towards an accurate and generalized detection of face morphing attacks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:22.236660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b321f9a-66ea-47ed-b9f1-f875664e906f · outbound

This paper cites Deep face repre- sentations for differential morphing attack detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Deep face repre- sentations for differential morphing attack detection,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:22.037298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4efa145d-c134-40a9-9441-172bded4f66a · outbound

This paper cites Differential anomaly detection for facial im- ages,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Differential anomaly detection for facial im- ages,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:21.888372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a65b63d2-5ca2-4b67-a797-0cfd25bde43c · outbound

This paper cites Face analysis technology evaluation (fate) part 4: Morph - performance of automated face morph detection,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face analysis technology evaluation (fate) part 4: Morph - performance of automated face morph detection,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:21.701540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 713d5fce-1dd4-4bf3-af2e-d81347806f70 · outbound

This paper cites Generating automatically print/scan textures for morphing attack detection applications,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Generating automatically print/scan textures for morphing attack detection applications,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:21.536158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 115e186d-6095-4598-a03c-772f52264687 · outbound

This paper cites Face demorphing,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Face demorphing,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:21.358967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80589a56-dcf0-44fb-bc58-16ef62520f78 · outbound

This paper cites Fd-gan: Face de-morphing generative adversarial network for restoring accomplice’s facial image,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Fd-gan: Face de-morphing generative adversarial network for restoring accomplice’s facial image,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:21.164338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5775915e-5b17-4800-828f-ca419ab4fb9c · outbound

This paper cites Border control morphing attack detection with a convolutional neural network de-morphing approach,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Border control morphing attack detection with a convolutional neural network de-morphing approach,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:20.911599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b5d88fe-0ffd-44d9-85a3-73d50c75864a · outbound

This paper cites Foundation models and biometrics: A survey and outlook,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Foundation models and biometrics: A survey and outlook,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:20.644502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2bc109c3-7556-4bba-b748-1e8454158855 · outbound

This paper cites ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:20.403444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a190650-2f32-4144-a469-f3ef0320395d · outbound

This paper cites How good is ChatGPT at face biometrics? a first look into recognition, soft biometrics, and explainability,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems How good is ChatGPT at face biometrics? a first look into recognition, soft biometrics, and explainability,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:20.180330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 21bdcc2c-5593-4c9c-8505-090ecdaa77d5 · outbound

This paper cites ChatGPT meets iris biometrics,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems ChatGPT meets iris biometrics,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:19.953595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84a56311-bddc-4c57-824a-18c2f1bc5c40 · outbound

This paper cites Aligning actions and walking to llm-generated textual descriptions,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Aligning actions and walking to llm-generated textual descriptions,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:19.750385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad72c0c6-f416-4d9f-84be-7b1d6b76c4ad · outbound

This paper cites Language Models are Few-Shot Learners.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Language Models are Few-Shot Learners

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:15.417736Z

Source-reported events for the cited work

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Observation a9a1f1cf-131d-4f5c-91e9-5c1af0cc707a · outbound

This paper cites Facexbench: Evaluating multimodal llms on face understanding,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Facexbench: Evaluating multimodal llms on face understanding,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:15.510480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5949db84-f93b-43b9-8df9-aca61cff5969 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems LLaMA: Open and Efficient Foundation Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:15.573371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d255024-4c25-4884-ac75-cbd4d90a1e69 · outbound

This paper cites Palm: Scaling language modeling with pathways,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Palm: Scaling language modeling with pathways,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:19.526087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9732e8d3-996e-4b9f-81fd-2957122d5bfc · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Chain-of-thought prompting elicits reasoning in large language models,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:19.286160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a0a93f87-dbb2-4a0e-b672-e47295212757 · outbound

This paper cites A Survey on In-context Learning.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems A Survey on In-context Learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:15.763306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 956919a0-919b-4a0d-a749-2411450ca933 · outbound

This paper cites Joint face detection and alignment using multitask cascaded convolutional networks,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Joint face detection and alignment using multitask cascaded convolutional networks,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:15.821815Z

Source-reported events for the cited work

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Observation ec8a5c18-9c72-4a2d-8be0-e2ceb2161589 · outbound

This paper cites Privacy-friendly synthetic data for the development of face morphing attack detectors,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Privacy-friendly synthetic data for the development of face morphing attack detectors,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:19.033680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation caf2c2d9-6989-49cc-b6eb-7255dd3ba08e · outbound

This paper cites Hugging face,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Hugging face,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:18.886226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation abc78344-93bb-4534-94cf-7b8a0cc3c087 · outbound

This paper cites PyTorch: An Imperative Style, High- Performance Deep Learning Library,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems PyTorch: An Imperative Style, High- Performance Deep Learning Library,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:18.718275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2bc60fe3-88be-415e-803c-f886f360bf8e · outbound

This paper cites VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.074287Z

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Unavailable: canonical work link unavailable.

Observation 37af84cf-e2e8-4547-a004-8aab72ea8307 · outbound

This paper cites Ovis: Structural Embedding Alignment for Multimodal Large Language Model.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.151049Z

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Observation 4cd8fa66-5bfa-4f4f-a128-d1cd0d32135b · outbound

This paper cites Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.211195Z

Source-reported events for the cited work

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Observation 761e36c5-443a-4372-9125-55c92f451246 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.276194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec4b4279-228a-4101-8159-99319114880d · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.347239Z

Source-reported events for the cited work

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Observation ca6d35f7-7ee3-4fef-bcd0-8c1fb287fa72 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.406756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27c58a0b-988a-464a-920c-da24804ba49b · outbound

This paper cites Ocrbench: on the hidden mystery of ocr in large multimodal models,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Ocrbench: on the hidden mystery of ocr in large multimodal models,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.483198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 966f5da7-ef51-4831-beb9-bc2f1ab7e0c0 · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.574853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 129697c5-4a4c-4ebc-933e-97ce9349468f · outbound

This paper cites Qwen2-vl-7b-instruct: Vision-language model,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Qwen2-vl-7b-instruct: Vision-language model,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:18.444254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc6a84d0-051e-48de-be9a-17b7ab9d5a2b · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.804172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03e80687-6f1a-4568-9e5a-14550e530b84 · outbound

This paper cites MiniCPM-v 2.6: A gpt-4v level multimodal large language model,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems MiniCPM-v 2.6: A gpt-4v level multimodal large language model,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:18.205979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation adec5307-bff3-4d51-9fa7-21126ac3a9bb · outbound

This paper cites Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.986690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac8979d5-86f7-46e0-b07e-45fc013ffa52 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Learning transferable visual models from natural language supervision,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:17.943481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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