An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers | Paper Citation Record

Observation 6e886422-6a9b-42ad-92d7-55c632bc7d70 · outbound

This paper cites Quantifying attention flow in transformers, 2020.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Quantifying attention flow in transformers, 2020

Reference 1

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Observation 294b1b84-298e-4af4-948b-d00cc65c65a1 · outbound

This paper cites Backpropagation and stochastic gradient descent method.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backpropagation and stochastic gradient descent method

Reference 2

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Observation bdecafb2-daa1-46b6-ac9d-5c4e9b80589b · outbound

This paper cites How to backdoor federated learning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers How to backdoor federated learning

Reference 3

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Observation 5a33122b-ca16-4628-8ea8-5b9098b9dc18 · outbound

This paper cites Transformer interpretability beyond attention visualization.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Transformer interpretability beyond attention visualization

Reference 4

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Observation 55a89499-23c8-41b4-9539-b0bd679094e5 · outbound

This paper cites Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Reference 5

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Observation 65b8d830-ff4f-4541-9080-102de21afda1 · outbound

This paper cites DeepInspect: A black-box trojan detection and mitigation frame- work for deep neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers DeepInspect: A black-box trojan detection and mitigation frame- work for deep neural networks

Reference 6

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Observation 7ceacadd-e728-4b43-99f9-c477983260e9 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 7

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Observation 011eb416-3c09-4da0-8231-28138e4c7cee · outbound

This paper cites Backdoor attacks and defenses for deep neural networks in outsourced cloud environments.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor attacks and defenses for deep neural networks in outsourced cloud environments

Reference 8

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Observation ea01ea05-c0d7-46cd-820d-8d0e311eef01 · outbound

This paper cites From QoS to QoE: A tutorial on video quality assessment.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers From QoS to QoE: A tutorial on video quality assessment

Reference 9

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Observation d567b98c-f4d8-44fd-9a73-4033356d590c · outbound

This paper cites SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

Reference 10

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Observation 03b97f2c-6127-4c90-8780-128960a738ff · outbound

This paper cites Defending backdoor attacks on vision transformer via patch processing.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Defending backdoor attacks on vision transformer via patch processing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.572161Z

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Observation 7523a822-849a-445f-8651-0978796d408e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation b213e805-7c08-4323-87b7-b45673f01383 · outbound

This paper cites Imagenette: A smaller subset of 10 easily classified classes from imagenet.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Imagenette: A smaller subset of 10 easily classified classes from imagenet

Reference 13

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Observation a4b1db0a-75a1-42bf-8a26-81bee5824e26 · outbound

This paper cites STRIP: A defence against trojan attacks on deep neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers STRIP: A defence against trojan attacks on deep neural networks

Reference 14

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Observation 900c59e9-4ac0-4410-9f0e-fd996b4650e3 · outbound

This paper cites Atteq-nn: Attention-based qoe-aware evasive backdoor attacks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Atteq-nn: Attention-based qoe-aware evasive backdoor attacks

Reference 15

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Observation b2d0d31e-d61f-4616-bbf3-172d3239e4bc · outbound

This paper cites Coordinated backdoor attacks against federated learning with model-dependent triggers.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Coordinated backdoor attacks against federated learning with model-dependent triggers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.519200Z

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Observation f5065418-69bf-427c-acba-48a1812ec277 · outbound

This paper cites Defense-resistant backdoor attacks against deep neural networks in outsourced cloud 15 environment.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Defense-resistant backdoor attacks against deep neural networks in outsourced cloud 15 environment

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.508644Z

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Observation 76b71f23-3f7e-4138-aef4-c0c763756517 · outbound

This paper cites Backdoor attacks and defenses in federated learning: State-of-the- art, taxonomy, and future directions.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor attacks and defenses in federated learning: State-of-the- art, taxonomy, and future directions

Reference 18

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Observation 373e6547-3a42-4921-b733-bdfbd952ac77 · outbound

This paper cites Redeem myself: Purifying backdoors in deep learning models using self attention distillation.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Redeem myself: Purifying backdoors in deep learning models using self attention distillation

Reference 19

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Observation d8c1c9e2-6caa-49f3-b471-2b5ded244958 · outbound

This paper cites BadNets: Evaluating backdooring attacks on deep neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers BadNets: Evaluating backdooring attacks on deep neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.470366Z

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Observation 3dac3433-1ae8-4c50-ac04-9c88ddc9f802 · outbound

This paper cites Attributes-guided and pure-visual attention alignment for few- shot recognition.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Attributes-guided and pure-visual attention alignment for few- shot recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.459046Z

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Observation 2bf65177-afef-4142-8322-bb743035f18c · outbound

This paper cites NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations

Reference 22

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Observation 49e0351e-5dba-46cf-9b57-6fd32dc7a02b · outbound

This paper cites Model-reuse attacks on deep learning systems.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Model-reuse attacks on deep learning systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.447495Z

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Observation 9bcc440b-ab7d-40be-a69f-32c31e046a22 · outbound

This paper cites Backdoor attacks against learning systems.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor attacks against learning systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.435790Z

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Observation c3d0ed00-40ab-4adc-86e0-b70386d3991e · outbound

This paper cites Adam: A method for stochastic optimization.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Adam: A method for stochastic optimization

Reference 25

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Observation 9dcda7e9-5f32-49f8-afb5-2ffd0b9da191 · outbound

This paper cites Bilinear interpolation.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Bilinear interpolation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.416530Z

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Observation cea22268-dfde-49a8-8f2b-a23fa3a48b7b · outbound

This paper cites Learning multiple layers of features from tiny images.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Learning multiple layers of features from tiny images

Reference 27

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Observation a3c963e7-b54b-4abd-9203-fdfa790b5f4e · outbound

This paper cites Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Reference 28

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Observation 61572129-3321-4611-8f34-43d4996202d2 · outbound

This paper cites Neural attention distillation: Erasing backdoor triggers from deep neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Neural attention distillation: Erasing backdoor triggers from deep neural networks

Reference 29

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Observation ba34ffd9-5651-4bd1-ac03-32f3b0c84602 · outbound

This paper cites Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Reference 30

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Observation f453c190-b306-49ff-82bf-1e16f15b89e4 · outbound

This paper cites Rethinking the Trigger of Backdoor Attack.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Rethinking the Trigger of Backdoor Attack

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T20:03:56.736750Z

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Observation 1f3832b6-7834-4c76-b478-1e4d1bee76c7 · outbound

This paper cites Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:03:56.980363Z

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Observation b03becf0-ae20-41cd-9039-ea5d72a8b77f · outbound

This paper cites Composite backdoor attack for deep neural network by mixing existing benign features.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Composite backdoor attack for deep neural network by mixing existing benign features

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.393631Z

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Observation 74358214-83ff-49d0-b97d-051b2c76b6e0 · outbound

This paper cites Backdoor attacks and defenses in feature-partitioned collaborative learning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor attacks and defenses in feature-partitioned collaborative learning

Reference 34

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Observation 6d48af17-8686-4bdf-8242-c5af1465ef25 · outbound

This paper cites ABS: Scanning neural networks for backdoors by artificial brain stimulation.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers ABS: Scanning neural networks for backdoors by artificial brain stimulation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.382552Z

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Observation ecfe0e5f-4f94-4383-8810-75bc1c60c8b8 · outbound

This paper cites Trojaning attack on neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Trojaning attack on neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.371847Z

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Observation 0b051119-5f2c-4e15-9268-ccb8616bc898 · outbound

This paper cites DBIA: Data-free Backdoor Injection Attack against Transformer Networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers DBIA: Data-free Backdoor Injection Attack against Transformer Networks

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:03:56.954507Z

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Observation 5be2094e-3c22-4117-b66d-558b085a0755 · outbound

This paper cites NIC: Detecting adversarial samples with neural network invariant checking.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers NIC: Detecting adversarial samples with neural network invariant checking

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.361199Z

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Observation 9e0e9290-07e9-4804-a7b2-215c561fd7d0 · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Distributed representations of words and phrases and their compositionality

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.350855Z

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Observation 871d685d-7558-4349-bc53-0fc04196971b · outbound

This paper cites Visual slam for automated driving: Exploring the applications of deep learning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Visual slam for automated driving: Exploring the applications of deep learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.338901Z

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Observation 377b3921-933a-49ce-9102-a4d3d2993734 · outbound

This paper cites Recurrent Models of Visual Attention.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Recurrent Models of Visual Attention

Reference 41

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Observation 59be5193-939a-4a6c-a335-b0d9ed1efa79 · outbound

This paper cites Machine learning with membership privacy using adversarial regularization.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Machine learning with membership privacy using adversarial regularization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.327827Z

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Observation 4deeec66-c4e4-4ee6-bdbf-3aece55f467b · outbound

This paper cites Input-aware dynamic backdoor attack.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Input-aware dynamic backdoor attack

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.317423Z

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Observation 3ec75a3b-7a50-4daf-b783-02fd98752e2d · outbound

This paper cites WaNet -- Imperceptible Warping-based Backdoor Attack.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers WaNet -- Imperceptible Warping-based Backdoor Attack

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T20:03:56.782562Z

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Observation 3a255342-8766-4039-8d6d-b5c28c0a43ec · outbound

This paper cites A tale of evil twins: Adversarial inputs versus poisoned models.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers A tale of evil twins: Adversarial inputs versus poisoned models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.306782Z

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Observation f1b95dd2-fb6e-430a-ab34-405e5f174845 · outbound

This paper cites You only look once: Unified, real-time object detection.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers You only look once: Unified, real-time object detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.294854Z

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Observation 17434b77-6e25-402e-8d84-1c75fd271934 · outbound

This paper cites Hidden trigger backdoor attacks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Hidden trigger backdoor attacks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.282228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c7e3e51f-370a-4fd5-ac79-a16f83695f8d · outbound

This paper cites Dynamic backdoor attacks against machine learning models.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Dynamic backdoor attacks against machine learning models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.270869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb5a2f7a-9bfb-4f15-88dd-46a7d8782d97 · outbound

This paper cites Facenet: A unified embedding for face recognition and clustering.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Facenet: A unified embedding for face recognition and clustering

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.260098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3a6235cb-f719-4286-a303-1f4c4b408e9a · outbound

This paper cites an unresolved cited work.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:03:57.248914Z

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Observation 3cafd878-0c7a-4f7f-8f94-fb7e74acc05f · outbound

This paper cites Backdoor Attacks on Vision Transformers.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor Attacks on Vision Transformers

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T20:03:56.806308Z

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Observation a3f90882-f3c4-4f0f-9cfc-dec5aeb28b27 · outbound

This paper cites Spectral signatures in backdoor attacks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Spectral signatures in backdoor attacks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.237324Z

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Observation cf56f384-c73a-4f62-839b-828927e4b93b · outbound

This paper cites Model Agnostic Defence against Backdoor Attacks in Machine Learning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Model Agnostic Defence against Backdoor Attacks in Machine Learning

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:03:56.905514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7ee0bcec-3ee7-42e6-b2a1-b87a49b5e964 · outbound

This paper cites Attention is all you need.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Attention is all you need

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T20:03:56.815636Z

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Observation beae34bd-5abc-4b2f-9ad2-7549f2e630bb · outbound

This paper cites Neural cleanse: Identifying and mitigating backdoor attacks in neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Neural cleanse: Identifying and mitigating backdoor attacks in neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.219851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 63cf0296-bb9a-4da4-b544-e4a005a5c56b · outbound

This paper cites Residual attention network for image classification.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Residual attention network for image classification

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.209493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 38ab6e9e-4b9b-415d-b28c-cb607a704eef · outbound

This paper cites Papailiopoulos.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Papailiopoulos

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.199286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db93f875-2504-4c88-87c6-844a754221f2 · outbound

This paper cites Backdoor attacks against transfer learning with pre-trained deep learning models.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Backdoor attacks against transfer learning with pre-trained deep learning models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.188955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77a81ecd-35c8-4c9f-a08a-93d8c2100515 · outbound

This paper cites Image quality assessment: From error visibility to structural 16 similarity.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Image quality assessment: From error visibility to structural 16 similarity

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.177902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f85af807-bd8c-4d8c-b27d-b3891b253ead · outbound

This paper cites DBA: Distributed backdoor attacks against federated learning.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers DBA: Distributed backdoor attacks against federated learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.167702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 02680687-54d8-40ef-afda-378339502df5 · outbound

This paper cites Detecting ai trojans using meta neural analysis.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Detecting ai trojans using meta neural analysis

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.157794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0166cafe-aa33-42bd-81d2-e41a7c12287b · outbound

This paper cites Countermeasure against backdoor attacks using epistemic classifiers.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Countermeasure against backdoor attacks using epistemic classifiers

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.147111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d56e994c-088c-4459-a582-74eb51aa2166 · outbound

This paper cites Latent backdoor attacks on deep neural networks.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Latent backdoor attacks on deep neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.136272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9c7d9184-e502-493e-b1a2-4b3eed4e5ea1 · outbound

This paper cites an unresolved cited work.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:03:57.125210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a12161e6-35e5-4be0-9c81-1acf8f744311 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers The unreasonable effectiveness of deep features as a perceptual metric

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.113528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff97482d-10eb-4104-bab9-b6cfb2b839cd · outbound

This paper cites Trojvit: Trojan insertion in vision transformers.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Trojvit: Trojan insertion in vision transformers

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.103750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 13b86801-f3cb-4f82-8865-4bc943f77448 · outbound

This paper cites Parallelized stochastic gradient descent.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Parallelized stochastic gradient descent

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.093179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d850e27e-3d1e-4207-8ee8-bf8670f4751b · outbound

This paper cites Top Minds.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers Top Minds

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.069965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a435342-2225-417e-b010-dd84db44e11b · outbound

This paper cites His research interests include the Internet of Things, smart sensing, and AI security.

An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers His research interests include the Internet of Things, smart sensing, and AI security

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:03:57.082823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Reference resolution

Outbound references

Pith citing papers