A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems | Paper Citation Record

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Unresolved cited work

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This paper cites Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,

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This paper cites Presentation attack detection methods for face recognition systems: A comprehensive survey,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Presentation attack detection methods for face recognition systems: A comprehensive survey,

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This paper cites Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems

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This paper cites Robust heterogeneous discriminative analysis for face recognition with single sample per person,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Robust heterogeneous discriminative analysis for face recognition with single sample per person,

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This paper cites Master face attacks on face recognition systems,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Master face attacks on face recognition systems,

Reference 6

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This paper cites Custom silicone face masks: Vulnerability of commercial face recognition systems & presentation attack detection,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Custom silicone face masks: Vulnerability of commercial face recognition systems & presentation attack detection,

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This paper cites Unravelling robustness of deep learning based face recognition against adversarial attacks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Unravelling robustness of deep learning based face recognition against adversarial attacks,

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This paper cites Towards deep learning models resistant to adversarial attacks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Towards deep learning models resistant to adversarial attacks,

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This paper cites Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax,

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This paper cites Formal guarantees on the robustness of a classifier against adversarial manipulation,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Formal guarantees on the robustness of a classifier against adversarial manipulation,

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This paper cites Limitations of the lipschitz constant as a defense against adversarial examples,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Limitations of the lipschitz constant as a defense against adversarial examples,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Lipschitz regularity of deep neural networks: analysis and efficient estimation,

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This paper cites Advbox: a toolbox to generate adversarial examples that fool neural networks.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Advbox: a toolbox to generate adversarial examples that fool neural networks

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This paper cites Explaining and Harnessing Adversarial Examples.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Explaining and Harnessing Adversarial Examples

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This paper cites Rdcface: Radial distortion correction for face recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Rdcface: Radial distortion correction for face recognition,

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This paper cites Why do adversarial attacks transfer? ex- plaining transferability of evasion and poisoning attacks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Why do adversarial attacks transfer? ex- plaining transferability of evasion and poisoning attacks,

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

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Enhancing the transferability of adversarial attacks through variance tuning,

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This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

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This paper cites Evaluating the robustness of neural networks: An extreme value theory approach,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Evaluating the robustness of neural networks: An extreme value theory approach,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Unresolved cited work

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Unresolved cited work

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This paper cites Chicco, Siamese Neural Networks: An Overview.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Chicco, Siamese Neural Networks: An Overview

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This paper cites A light cnn for deep face representation with noisy labels,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems A light cnn for deep face representation with noisy labels,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Vision transformer with deformable attention,

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This paper cites SoK: Certified Robustness for Deep Neural Networks.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems SoK: Certified Robustness for Deep Neural Networks

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Robustness and accuracy could be reconcilable by (proper) definition,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Characterizing adversarial subspaces using local intrinsic dimensionality,

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This paper cites A survey on: Facial emotion recognition invariant to pose, illumination and age,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems A survey on: Facial emotion recognition invariant to pose, illumination and age,

Reference 29

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Certified robust accuracy of neural networks are bounded due to bayes errors,

Reference 30

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Searching for a search method: Benchmarking search algorithms for generating NLP adversarial examples,

Reference 32

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This paper cites Efficient decision-based black-box adversarial attacks on face recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Efficient decision-based black-box adversarial attacks on face recognition,

Reference 33

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems On Evaluating Adversarial Robustness

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Adversarial machine learning at scale,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks,

Reference 36

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Intriguing properties of neural networks

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Towards evaluating the robustness of neural networks,

Reference 38

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Probabilistically robust learning: Balancing average and worst-case performance,

Reference 39

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Practical black-box attacks against machine learning,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,

Reference 41

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems One pixel attack for fooling deep neural networks,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Genattack: Practical black-box attacks with gradient- free optimization,

Reference 43

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This paper cites An abstract domain for certifying neural networks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems An abstract domain for certifying neural networks,

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Reluplex: An efficient smt solver for verifying deep neural networks,

Reference 45

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This paper cites Unleash the black magic in age: A multi-task deep neural network approach for cross-age face verification,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Unleash the black magic in age: A multi-task deep neural network approach for cross-age face verification,

Reference 46

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This paper cites Advhat: Real-world adversarial attack on arcface face id system,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Advhat: Real-world adversarial attack on arcface face id system,

Reference 47

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This paper cites Cross-pose lfw: A database for studying cross- pose face recognition in unconstrained environments,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Cross-pose lfw: A database for studying cross- pose face recognition in unconstrained environments,

Reference 48

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This paper cites Illumination invariant face recog- nition: A survey,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Illumination invariant face recog- nition: A survey,

Reference 49

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This paper cites Mlfw: A database for face recognition on masked faces,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Mlfw: A database for face recognition on masked faces,

Reference 50

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This paper cites Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments,

Reference 51

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A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Bridging the Performance Gap between FGSM and PGD Adversarial Training

Reference 52

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This paper cites Facenet: A unified embed- ding for face recognition and clustering,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Facenet: A unified embed- ding for face recognition and clustering,

Reference 53

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This paper cites Distance metric learning for large margin nearest neighbor classification,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Distance metric learning for large margin nearest neighbor classification,

Reference 54

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

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Arcface: Additive angular margin loss for deep face recognition,

Reference 55

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This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems EfficientNet: Rethinking model scaling for convolutional neural networks,

Reference 56

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

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Mis-classified vector guided softmax loss for face recognition,

Reference 57

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This paper cites Rethinking channel dimensions for efficient model design,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Rethinking channel dimensions for efficient model design,

Reference 58

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

Observation 63a0545c-300a-489f-91e5-9e4fd055a471 · outbound

This paper cites Curricularface: Adaptive curriculum learning loss for deep face recog- nition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Curricularface: Adaptive curriculum learning loss for deep face recog- nition,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.910945Z

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 7de31d3b-8019-4e1d-b324-af873e75deff · outbound

This paper cites Residual attention network for image classification,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Residual attention network for image classification,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.889004Z

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 9336a9da-1f2e-47fa-bd9c-d32eda9801ea · outbound

This paper cites Adacos: Adaptively scaling cosine logits for effectively learning deep face representations,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Adacos: Adaptively scaling cosine logits for effectively learning deep face representations,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.869709Z

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 f379df24-a7e4-4027-91f3-6ef0cbb85382 · outbound

This paper cites Ghostnet: More features from cheap operations,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Ghostnet: More features from cheap operations,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.848609Z

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 3c7ee366-efd0-4eaf-8acf-02074e06b12f · outbound

This paper cites Additive margin softmax for face verification,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Additive margin softmax for face verification,

Reference 63

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Observation b797bbc4-6ee7-49cc-9f8a-3807a3cd1b87 · outbound

This paper cites Repvgg: Mak- ing vgg-style convnets great again,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Repvgg: Mak- ing vgg-style convnets great again,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.815217Z

Source-reported events for the cited work

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Observation 1f4438cd-bf4e-4f05-9dd2-21dc03a8e895 · outbound

This paper cites Tf-nas: Rethinking three search freedoms of latency-constrained differentiable neural architecture search,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Tf-nas: Rethinking three search freedoms of latency-constrained differentiable neural architecture search,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.796277Z

Source-reported events for the cited work

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Observation d4af542a-cf43-45b9-9a1c-118fcf9f38c2 · outbound

This paper cites Frontal to profile face verification in the wild,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Frontal to profile face verification in the wild,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.774883Z

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 5e78fed3-afa2-4e78-9899-1a65aeadce8e · outbound

This paper cites Agedb: The first manually collected, in-the-wild age database,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Agedb: The first manually collected, in-the-wild age database,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.750330Z

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 27a5a5f4-381e-414a-8d90-718f897fba6e · outbound

This paper cites Mirjalili, Genetic Algorithm.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Mirjalili, Genetic Algorithm

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.728527Z

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

Observation 4d272e6b-ade8-4a75-af3e-d5620de18bf1 · outbound

This paper cites Pymoo: Multi-objective optimization in python,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Pymoo: Multi-objective optimization in python,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:53.254106Z

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Observation 874b9bad-b329-4b91-899a-461e70dcaf69 · outbound

This paper cites Proa: A probabilistic robustness assessment against functional perturbations,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Proa: A probabilistic robustness assessment against functional perturbations,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.693879Z

Source-reported events for the cited work

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Observation 639c52ab-0ab0-459b-84c4-1715607b53e2 · outbound

This paper cites Deep face recognition: A survey,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Deep face recognition: A survey,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.674088Z

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Observation 0878de0c-c7b8-4583-9a9f-07322ed4b902 · outbound

This paper cites Deep metric learning using triplet network,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Deep metric learning using triplet network,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.654837Z

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 02b976bc-b8fa-4887-b078-42f4c1932774 · outbound

This paper cites Adaptiveface: Adaptive margin and sampling for face recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Adaptiveface: Adaptive margin and sampling for face recognition,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.633023Z

Source-reported events for the cited work

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Observation c1dad790-30f4-4c48-850d-15d563342cfd · outbound

This paper cites Improved performance of face recognition using cnn with constrained triplet loss layer,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Improved performance of face recognition using cnn with constrained triplet loss layer,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.613782Z

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 49d7b811-d83b-498c-bec1-63399fccec8f · outbound

This paper cites Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.595293Z

Source-reported events for the cited work

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Observation acaaf176-87cf-42be-a005-16cccf141e2e · outbound

This paper cites Deep metric learning with hierarchical triplet loss,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Deep metric learning with hierarchical triplet loss,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.566774Z

Source-reported events for the cited work

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Observation 550fbc64-1c7d-46c7-bf73-326f89fff504 · outbound

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

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Cosface: Large margin cosine loss for deep face recognition,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.546657Z

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 3aaa561f-1400-4cd5-9b66-542057e070b8 · outbound

This paper cites Deep residual learning for image recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Deep residual learning for image recognition,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:53.330590Z

Source-reported events for the cited work

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Observation 1ab6de85-aea8-4ea7-bb49-b4bb27a2a24f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.513726Z

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 0d3d65a0-bfe0-4e00-9097-0d954dad7218 · outbound

This paper cites Sphereface: Deep hypersphere embedding for face recognition,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Sphereface: Deep hypersphere embedding for face recognition,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.494554Z

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 48f5ef17-5294-4c84-a84a-e9c79a7f2d9e · outbound

This paper cites Universal pertur- bation attack against image retrieval,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Universal pertur- bation attack against image retrieval,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.474741Z

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 e3313ea4-a0cd-4ff2-b10d-6f6c7abd4a63 · outbound

This paper cites How benign is benign overfitting ?.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems How benign is benign overfitting ?

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.456693Z

Source-reported events for the cited work

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Observation 5f67d155-58bf-4402-88f3-4a6ada3900fc · outbound

This paper cites Causality-based neural network repair,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Causality-based neural network repair,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:53.374818Z

Source-reported events for the cited work

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Observation 256f57cc-d58e-43e7-a69f-97f759a5017e · outbound

This paper cites Code Smells in Machine Learning Systems.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Code Smells in Machine Learning Systems

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:53.382201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0881dc01-0155-4899-baa2-8476caca9f6e · outbound

This paper cites Combining experts’ causal judgments,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Combining experts’ causal judgments,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.435843Z

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 e942a33d-30ad-455e-846b-9bbba3d0cae1 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Benchmarking neural network robustness to common corruptions and perturbations,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.408892Z

Source-reported events for the cited work

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Observation 32ac19c0-2e6a-428c-8a2d-4e3dd63654b0 · outbound

This paper cites Robot: Robustness-oriented testing for deep learning systems,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Robot: Robustness-oriented testing for deep learning systems,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.386620Z

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 4fbf16d8-e563-43ab-aa7c-8483ec1d9592 · outbound

This paper cites Biasfinder: Metamorphic test generation to uncover bias for sentiment JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 15 analysis systems,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Biasfinder: Metamorphic test generation to uncover bias for sentiment JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 15 analysis systems,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.362420Z

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 86e00aea-5085-4a07-9297-518c9e64227d · outbound

This paper cites A systematic literature review on the use of deep learning in software engineering research,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems A systematic literature review on the use of deep learning in software engineering research,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:53.422712Z

Source-reported events for the cited work

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Observation 2a505a4d-f02e-4609-8360-c966633462b3 · outbound

This paper cites AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:09:53.672702Z

Source-reported events for the cited work

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Observation 34743df5-5943-4361-9cb4-2f22976bd7f2 · outbound

This paper cites A probabilistic framework for mutation testing in deep neural networks,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems A probabilistic framework for mutation testing in deep neural networks,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.335936Z

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 4ff753c2-6aa8-4ce1-8c61-ffa4a6508a38 · outbound

This paper cites Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration,.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration,

Reference 92

Resolution: verified exact; doi, observed 2026-08-16T05:09:53.511133Z

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 74fc79f5-b685-4660-9e33-e37a9f007495 · outbound

This paper cites 18 667–18 686.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems 18 667–18 686

Reference 162

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:55.230549Z

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 2f6d6ac6-4801-4b75-96ce-f89cff17034e · outbound

This paper cites Available: https://proceedings.neurips.cc/paper/2005/ file/a7f592cef8b130a6967a90617db5681b-Paper.pdf.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Available: https://proceedings.neurips.cc/paper/2005/ file/a7f592cef8b130a6967a90617db5681b-Paper.pdf

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.996211Z

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 f295c827-2705-434c-92e2-6f57729e0b6c · outbound

This paper cites He is the co-founder of the PAT model checker.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems He is the co-founder of the PAT model checker

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:09:54.313987Z

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 1f3b63b8-58e3-42d3-91df-dcd5e8823484 · outbound

This paper cites Available: https://proceedings.neurips.cc/paper/2017/ file/e077e1a544eec4f0307cf5c3c721d944-Paper.pdf.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Available: https://proceedings.neurips.cc/paper/2017/ file/e077e1a544eec4f0307cf5c3c721d944-Paper.pdf

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:52.839417Z

Source-reported events for the cited work

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Observation c3c15401-6598-4df3-a1cd-2b60c7b57b2e · outbound

This paper cites Adversarial Examples in Modern Machine Learning: A Review.

A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems Adversarial Examples in Modern Machine Learning: A Review

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T05:09:52.978291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers