Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation | Paper Citation Record

Observation c59954a6-2e32-40e8-bc46-95e5990c49b9 · outbound

This paper cites Partial FC: training 10 million identities on a single machine.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Partial FC: training 10 million identities on a single machine

Reference 1

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This paper cites Idiff-face: Synthetic-based face recognition through fizzy identity-conditioned diffusion models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Idiff-face: Synthetic-based face recognition through fizzy identity-conditioned diffusion models

Reference 2

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Observation 2f36f0c6-226c-4e93-8540-f443289ca3ed · outbound

This paper cites Unrestricted Adversarial Examples.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Unrestricted Adversarial Examples

Reference 3

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This paper cites An adaptive model ensemble adversarial attack for boosting adversarial transferability.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation An adaptive model ensemble adversarial attack for boosting adversarial transferability

Reference 4

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This paper cites Content-based unrestricted adver- sarial attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Content-based unrestricted adver- sarial attack

Reference 5

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This paper cites Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition

Reference 6

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Observation 80820cef-01cf-4613-b3d4-0ba21ffe18f1 · outbound

This paper cites Towards solving the deepfake problem: An analysis on improving deepfake detection using dynamic face augmentation.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards solving the deepfake problem: An analysis on improving deepfake detection using dynamic face augmentation

Reference 7

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Observation eb6f2d28-94c5-49c2-83eb-a5f27c369f4c · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Arcface: Additive angular margin loss for deep face recognition

Reference 8

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Observation 8fe92db0-059a-4242-afcd-dc846e698b2a · outbound

This paper cites Boosting adversarial attacks with momentum.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Boosting adversarial attacks with momentum

Reference 9

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Observation 3913dce6-53bb-48fd-90a9-0f54b032296e · outbound

This paper cites Improving the transferability of adversarial examples with arbitrary style transfer.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Improving the transferability of adversarial examples with arbitrary style transfer

Reference 10

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Observation abf20aaa-4893-400f-9722-c786f7220e81 · outbound

This paper cites Explaining and harnessing adversarial examples.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Explaining and harnessing adversarial examples

Reference 11

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Observation 3046de18-0231-4305-9af7-b244df6b7770 · outbound

This paper cites Lgv: Boosting adversarial example transferability from large geometric vicinity.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Lgv: Boosting adversarial example transferability from large geometric vicinity

Reference 12

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Observation 802e57a2-3094-4cee-a3bc-cc5e56122adc · outbound

This paper cites Deep residual learning for image recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Deep residual learning for image recognition

Reference 13

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Observation 46f557ed-ed25-498e-9412-f499cdfc7af0 · outbound

This paper cites Squeeze-and-excitation networks.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Squeeze-and-excitation networks

Reference 14

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Observation 303df7dd-f0fa-4abf-8807-ceaf7f0778d5 · outbound

This paper cites Protecting facial pri- vacy: Generating adversarial identity masks via style-robust makeup transfer.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Protecting facial pri- vacy: Generating adversarial identity masks via style-robust makeup transfer

Reference 15

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Observation aa1679ff-eeb5-4dea-87fb-6a0e3b1daec7 · outbound

This paper cites Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller

Reference 16

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Observation 672ee2da-d0d9-41fd-9408-94c7883a8ee9 · outbound

This paper cites Curricularface: Adaptive curriculum learning loss for deep face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Curricularface: Adaptive curriculum learning loss for deep face recognition

Reference 17

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This paper cites Adv-attribute: Inconspicuous and transferable adversarial attack on face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adv-attribute: Inconspicuous and transferable adversarial attack on face recognition

Reference 18

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This paper cites Progressive growing of gans for improved quality, stability, and variation.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Progressive growing of gans for improved quality, stability, and variation

Reference 19

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Observation fb3357fb-e394-4b28-a887-5dd2d0f20bb3 · outbound

This paper cites Rethinking feature- based knowledge distillation for face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Rethinking feature- based knowledge distillation for face recognition

Reference 20

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Observation df8d0c84-d176-441b-aa79-1ecbc60812a2 · outbound

This paper cites Physical-world optical adversarial attacks on 3d face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Physical-world optical adversarial attacks on 3d face recognition

Reference 21

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This paper cites Sibling-attack: Rethinking transferable adversarial attacks against face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Sibling-attack: Rethinking transferable adversarial attacks against face recognition

Reference 22

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Observation ab3c1b58-21c2-4d3f-9638-dfc6346d6d14 · outbound

This paper cites Adversarial example does good: Preventing painting imitation 9 from diffusion models via adversarial examples.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adversarial example does good: Preventing painting imitation 9 from diffusion models via adversarial examples

Reference 23

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This paper cites Hopcroft.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Hopcroft

Reference 24

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This paper cites Enhancing generalization of universal adversarial perturbation through gradient aggregation.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Enhancing generalization of universal adversarial perturbation through gradient aggregation

Reference 25

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Observation bc6fd12b-3bec-4533-adb4-1a82c788ab38 · outbound

This paper cites TRM-UAP: enhancing the transferability of data-free univer- sal adversarial perturbation via truncated ratio maximization.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation TRM-UAP: enhancing the transferability of data-free univer- sal adversarial perturbation via truncated ratio maximization

Reference 26

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Observation 0d219478-d014-43ac-b931-385667e11a1f · outbound

This paper cites Frequency domain model augmentation for adversarial attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Frequency domain model augmentation for adversarial attack

Reference 27

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Observation 44fe9440-8ad7-42bb-9b57-08e7410876fa · outbound

This paper cites Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models

Reference 28

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This paper cites Magface: A universal representation for face recognition and quality assessment.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Magface: A universal representation for face recognition and quality assessment

Reference 29

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This paper cites Towards multiple black-boxes attack via adversarial example generation network.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards multiple black-boxes attack via adversarial example generation network

Reference 30

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This paper cites Df-platter: Multi- face heterogeneous deepfake dataset.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Df-platter: Multi- face heterogeneous deepfake dataset

Reference 31

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This paper cites Dynamic routing and knowledge re- learning for data-free black-box attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Dynamic routing and knowledge re- learning for data-free black-box attack

Reference 32

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This paper cites Semanticadv: Generating adversarial exam- ples via attribute-conditioned image editing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Semanticadv: Generating adversarial exam- ples via attribute-conditioned image editing

Reference 33

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Observation 1ff50ed3-419f-49b5-b0ff-8ac348ef74c4 · outbound

This paper cites Facenet: A unified embedding for face recognition and clus- tering.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Facenet: A unified embedding for face recognition and clus- tering

Reference 34

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Observation 58b3636e-2551-489d-92ad-bb8b987de0f5 · outbound

This paper cites Clip2protect: Protecting facial privacy using text-guided makeup via adversarial latent search.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Clip2protect: Protecting facial privacy using text-guided makeup via adversarial latent search

Reference 35

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This paper cites Jail- break in pieces: Compositional adversarial attacks on multi- modal language models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Jail- break in pieces: Compositional adversarial attacks on multi- modal language models

Reference 36

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This paper cites Benchmarking robustness to adversarial image ob- fuscations.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Benchmarking robustness to adversarial image ob- fuscations

Reference 37

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This paper cites Circle loss: A unified perspective of pair similarity optimization.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Circle loss: A unified perspective of pair similarity optimization

Reference 38

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Observation 3d2a099f-fc6d-4177-bbdc-d35186490eb5 · outbound

This paper cites Diffam: Diffusion-based adversarial makeup transfer for facial privacy protection.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Diffam: Diffusion-based adversarial makeup transfer for facial privacy protection

Reference 39

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Observation 763d0dec-aa93-4b7c-9df8-3ab6b20988ef · outbound

This paper cites ACTIVE: towards highly transferable 3d physical camouflage for universal and robust vehicle evasion.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation ACTIVE: towards highly transferable 3d physical camouflage for universal and robust vehicle evasion

Reference 40

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Observation 9fcb8012-0ce1-4669-ac38-d447fdcac74a · outbound

This paper cites Teachaugment: Data augmentation opti- mization using teacher knowledge.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Teachaugment: Data augmentation opti- mization using teacher knowledge

Reference 41

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Observation 86c5d436-ce11-407e-a254-00af5017523d · outbound

This paper cites Goodfellow, and Rob Fergus.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Goodfellow, and Rob Fergus

Reference 42

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This paper cites RFLA: A stealthy reflected light adversarial attack in the physical world.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation RFLA: A stealthy reflected light adversarial attack in the physical world

Reference 43

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Observation 40c9cc64-3de8-47be-9916-dbb5137b2cd3 · outbound

This paper cites Cosface: Large margin cosine loss for deep face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Cosface: Large margin cosine loss for deep face recognition

Reference 44

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Observation 9cbc38ab-11d7-4cfd-8b63-9a5a4722c020 · outbound

This paper cites Facex-zoo: A pytorh toolbox for face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Facex-zoo: A pytorh toolbox for face recognition

Reference 45

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Observation 7ee335e2-cfa2-445b-b2b0-a413e0bae264 · outbound

This paper cites Boosting Adversarial Transferability by Block Shuffle and Rotation.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Boosting Adversarial Transferability by Block Shuffle and Rotation

Reference 46

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Observation dd08fa40-dc69-4cca-8aef-523d39a6dd2e · outbound

This paper cites Mitigating bias in face recog- nition using skewness-aware reinforcement learning.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Mitigating bias in face recog- nition using skewness-aware reinforcement learning

Reference 47

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Observation fa11bd0f-22e6-42ca-a66f-973c319106aa · outbound

This paper cites Deep face recognition: A survey.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Deep face recognition: A survey

Reference 48

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Observation fdd2c3b6-93a1-4412-8dd4-172015a34977 · outbound

This paper cites Racial faces in the wild: Reducing racial bias by information maximization adaptation network.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Racial faces in the wild: Reducing racial bias by information maximization adaptation network

Reference 49

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Observation f54a606c-1994-4252-a007-431bfe852ed3 · outbound

This paper cites Meta balanced network for fair face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Meta balanced network for fair face recognition

Reference 50

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Observation 43532d59-c02a-40c9-8d80-9ca96d9c0101 · outbound

This paper cites Delving into data: Effectively substitute training for black-box attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Delving into data: Effectively substitute training for black-box attack

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.983123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 940e8bda-2d9a-400d-b196-efd0b42b7a0c · outbound

This paper cites Dst: Dynamic substitute training for data-free black- box attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Dst: Dynamic substitute training for data-free black- box attack

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.946213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0770ceb6-00fa-4767-bb63-f6df7a1661d0 · outbound

This paper cites Enhancing the transferability of adversarial attacks through variance tuning.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Enhancing the transferability of adversarial attacks through variance tuning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.918626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b09fba5-9f76-42ff-97c3-116d2488a1f2 · outbound

This paper cites Mis-classified vector guided softmax loss for face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Mis-classified vector guided softmax loss for face recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.883740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ece2e10-68f5-47ba-851c-737994c1633f · outbound

This paper cites Structure invariant transformation for better adversarial transferability.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Structure invariant transformation for better adversarial transferability

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.847043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fb2daff8-f315-43c6-b4ce-57dad90241b5 · outbound

This paper cites Tf-fas: Twofold-element fine-grained semantic guidance for general- izable face anti-spoofing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Tf-fas: Twofold-element fine-grained semantic guidance for general- izable face anti-spoofing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.819807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e72f9ea3-38d7-4837-8e92-387ec4b60c67 · outbound

This paper cites Uni- fied adversarial patch for cross-modal attacks in the physical world.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Uni- fied adversarial patch for cross-modal attacks in the physical world

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.790971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bb0face3-1ac3-4f06-b328-c595f59400e3 · outbound

This paper cites Physically adversarial infrared patches with learnable shapes and locations.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Physically adversarial infrared patches with learnable shapes and locations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.762521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5bdd1394-f92b-447c-973f-694973bea4de · outbound

This paper cites Beneficial Perturbations Network for Defending Adversarial Examples.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Beneficial Perturbations Network for Defending Adversarial Examples

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:14:38.928963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7822a1b-6339-479d-9fa3-b4b6a1fa38fb · outbound

This paper cites Im- proving transferable targeted adversarial attacks with model self-enhancement.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Im- proving transferable targeted adversarial attacks with model self-enhancement

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.729730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c81689fa-37ab-40aa-a75b-2fdc72b11424 · outbound

This paper cites Improving transferability of adversarial patches on face recognition with generative models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Improving transferability of adversarial patches on face recognition with generative models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.701477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27b7daae-0937-48c6-9bb9-5f213cbe945c · outbound

This paper cites an unresolved cited work.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:14:39.670641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3e9397d2-7795-48db-9220-501f82261de5 · outbound

This paper cites Backpropagation path search on adversarial transferability.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Backpropagation path search on adversarial transferability

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.647138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ae3fe2f-8f8d-4296-9eef-3fabb6271390 · outbound

This paper cites Towards face encryption by generating adversarial identity masks.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards face encryption by generating adversarial identity masks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.610120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0c7d8114-a42c-4d5c-9ae8-337287ea88b7 · outbound

This paper cites Towards effective adversarial textured 3d meshes on physical face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards effective adversarial textured 3d meshes on physical face recognition

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.577208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ccacb050-4531-4d4b-8029-5f05cddc49bb · outbound

This paper cites Adv- makeup: A new imperceptible and transferable attack on face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adv- makeup: A new imperceptible and transferable attack on face recognition

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.546570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43d68ae8-7f39-4bb2-9ba0-b8ee220a3d11 · outbound

This paper cites Natural color fool: Towards boosting black-box unrestricted attacks.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Natural color fool: Towards boosting black-box unrestricted attacks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.524523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 73e28479-bb78-4bbe-b443-07bc66c02852 · outbound

This paper cites Npcface: Negative-positive collaborative training for large-scale face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Npcface: Negative-positive collaborative training for large-scale face recognition

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.484337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a485259-5118-40c3-acea-9774e252032c · outbound

This paper cites Towards adversarial at- tack on vision-language pre-training models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards adversarial at- tack on vision-language pre-training models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.447900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c095da32-05b8-4648-8908-264d148d78cc · outbound

This paper cites Adversarial autoaugment.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adversarial autoaugment

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.412744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c4fc488c-0985-4a83-ab98-16c8939c0663 · outbound

This paper cites Adversarial learning with margin-based triplet embedding regularization.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adversarial learning with margin-based triplet embedding regularization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.386871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a08eee4-5d34-45c6-b642-d5990942aca3 · outbound

This paper cites Towards transferable adversarial attack against deep face recognition.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Towards transferable adversarial attack against deep face recognition

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.357138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 90a59fef-c50c-47b7-80ca-d9563bfef7f1 · outbound

This paper cites Improving visual quality and transferability of ad- versarial attacks on face recognition simultaneously with ad- versarial restoration.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Improving visual quality and transferability of ad- versarial attacks on face recognition simultaneously with ad- versarial restoration

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.334611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c74da496-cc1c-4cbb-8c43-858bb0d94e8e · outbound

This paper cites Improving the transferability of adver- sarial attacks on face recognition with beneficial perturbation feature augmentation.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Improving the transferability of adver- sarial attacks on face recognition with beneficial perturbation feature augmentation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.308767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 511e645f-55da-405f-b8e4-4339f3d62379 · outbound

This paper cites Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:14:38.877176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03e9f3c8-21bc-4ace-97de-098ad06f85de · outbound

This paper cites Rethinking imper- sonation and dodging attacks on face recognition systems.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Rethinking imper- sonation and dodging attacks on face recognition systems

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.277346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81569d8a-f6b4-4533-928f-423b76e48d62 · outbound

This paper cites Adaptive mixture of experts learning for generalizable face anti-spoofing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Adaptive mixture of experts learning for generalizable face anti-spoofing

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.238631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 77822af6-ef75-4958-93bd-4f36a930d7bd · outbound

This paper cites Generative do- main adaptation for face anti-spoofing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Generative do- main adaptation for face anti-spoofing

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.209381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e753dfc-667c-4f3d-b860-a2eea9a90dc0 · outbound

This paper cites Instance-aware domain generalization for face anti-spoofing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Instance-aware domain generalization for face anti-spoofing

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.175531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8eaa26fb-2715-4ffe-8fab-87295a20bc73 · outbound

This paper cites Test-time domain gen- eralization for face anti-spoofing.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Test-time domain gen- eralization for face anti-spoofing

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.137293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 764b977a-122a-4391-b247-f8531e1ef361 · outbound

This paper cites Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.102630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7970d548-ceab-414b-992a-f0af826cde91 · outbound

This paper cites Boosting adversarial transferability via gradi- ent relevance attack.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Boosting adversarial transferability via gradi- ent relevance attack

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:14:39.062860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bffb984b-f0d6-46ea-9879-9890a1f8f760 · outbound

This paper cites The supplementary includes the following sec- tions: • Section 7.1.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation The supplementary includes the following sec- tions: • Section 7.1

Reference 84

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T14:14:38.980599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 07c2b73b-1ef1-4c96-8f4e-d792b9ad7806 · outbound

This paper cites an unresolved cited work.

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:14:39.032417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

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