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Paper Citation Record · LEDGER

Attribution for Enhanced Explanation with Transferable Adversarial eXploration

As of 20 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2412.19523.

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pith.paper-citation-record.v1
2412.19523 v1

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measured 54 of 54 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 81a12d11-1166-4629-acd4-e62ca0c4668c · outbound

This paper cites Deep learning,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Deep learning,

Reference 1

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Observation fd7319ed-4cd2-4a9e-b625-30adeb043f0e · outbound

This paper cites Dermatologist-level classification of skin cancer with deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Dermatologist-level classification of skin cancer with deep neural networks,

Reference 2

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Observation 74602d3b-ddf6-478c-b8d9-c2d6950fcf7c · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Xgboost: A scalable tree boosting system,

Reference 3

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Observation bfe390d8-f450-40c1-adcf-5fbe288b5f38 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Towards A Rigorous Science of Interpretable Machine Learning

Reference 4

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Observation 4f12712a-50f3-4bca-825c-ba681aa0b595 · outbound

This paper cites The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.,

Reference 5

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Observation de40d7da-d3ca-43ea-9644-a4b6fe3d4be6 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

Reference 6

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Observation 130f19eb-26b6-4fc8-b1b4-fd9d1445aa66 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Explaining and Harnessing Adversarial Examples

Reference 7

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Observation e5865bd0-4332-4f7b-a196-ec5bd90cc525 · outbound

This paper cites A survey on bias and fairness in machine learning,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration A survey on bias and fairness in machine learning,

Reference 8

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Observation 903e0cf6-52ad-453d-8f62-0536e2e71bb4 · outbound

This paper cites Propagating transparency: A deep dive into the interpretability of neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Propagating transparency: A deep dive into the interpretability of neural networks,

Reference 9

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This paper cites A benchmark for interpretability methods in deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration A benchmark for interpretability methods in deep neural networks,

Reference 10

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Observation fafb6085-a30c-4b3d-ad57-a696d308ddae · outbound

This paper cites Methods for interpreting and understanding deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Methods for interpreting and understanding deep neural networks,

Reference 11

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Observation b41d6f55-e1d4-4939-9d78-209da470d453 · outbound

This paper cites A survey on explainable artificial intelligence (xai): Toward medical xai,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration A survey on explainable artificial intelligence (xai): Toward medical xai,

Reference 12

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Observation 4e1060f6-006f-4ba1-b90b-e8600fee59f1 · outbound

This paper cites Interpretability of machine learning methods applied to neuroimaging,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Interpretability of machine learning methods applied to neuroimaging,

Reference 13

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Observation c74a40af-f1f6-4d6c-b736-1d1e7beabbb5 · outbound

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Simple black-box adversarial attacks on deep neural networks.,

Reference 14

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Observation 71d0a27b-a704-40c4-81ea-bda7f46a0c65 · outbound

This paper cites Di-aa: An interpretable white-box attack for fooling deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Di-aa: An interpretable white-box attack for fooling deep neural networks,

Reference 15

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Observation 59a5f3bb-e648-4ecd-974e-cb198cb34dd0 · outbound

This paper cites Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gra- dients,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gra- dients,

Reference 16

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Observation 213bc81b-f467-4857-a030-b67310c4fa7d · outbound

This paper cites Fooling network inter- pretation in image classification,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Fooling network inter- pretation in image classification,

Reference 17

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Observation fd313251-581a-41ff-85d2-f2d7dbf24f6f · outbound

This paper cites Adversarial perturbation defense on deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Adversarial perturbation defense on deep neural networks,

Reference 18

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Observation 8404bf02-275a-4d73-a8ae-12a1489104a2 · outbound

This paper cites Interpreting adversarial examples in deep learning: A review,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Interpreting adversarial examples in deep learning: A review,

Reference 19

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Observation a4b5456c-6bca-46da-94a2-333ac95a3f09 · outbound

This paper cites Attexplore: Attribution for explanation with model parameters exploration,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Attexplore: Attribution for explanation with model parameters exploration,

Reference 20

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Observation c70593ce-6006-4b6f-84f6-ca98bf6a8a6f · outbound

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration ” why should i trust you?

Reference 21

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Observation 41411573-fd11-44b3-b8c1-829f89a9a793 · outbound

This paper cites A unified approach to interpreting model predictions,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration A unified approach to interpreting model predictions,

Reference 22

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Observation 34a3ba29-e7bb-4f98-a4ed-f7c602aded6f · outbound

This paper cites Learning important features through propagating activation differences,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Learning important features through propagating activation differences,

Reference 23

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Observation 4e5cc2b9-3b4c-4b0d-95c4-e699ce92d00d · outbound

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Axiomatic attribution for deep networks,

Reference 24

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Observation 7ad29b38-883e-4c0b-abd0-6153f4bbe03a · outbound

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 25

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration SmoothGrad: removing noise by adding noise

Reference 26

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This paper cites Guided integrated gradients: An adaptive path method for removing noise,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Guided integrated gradients: An adaptive path method for removing noise,

Reference 27

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This paper cites Improving performance of deep learning models with axiomatic attribution priors and expected gradients,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Improving performance of deep learning models with axiomatic attribution priors and expected gradients,

Reference 28

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Robust Models Are More Interpretable Because Attributions Look Normal

Reference 29

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Fast axiomatic attribution for neural networks,

Reference 30

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This paper cites Explaining deep neural network models with adversarial gradient integration,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Explaining deep neural network models with adversarial gradient integration,

Reference 31

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This paper cites MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks

Reference 32

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This paper cites Iterative search attribution for deep neural networks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Iterative search attribution for deep neural networks,

Reference 33

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Observation 24c357fc-b3ca-41f8-9208-b91e9a0f4e27 · outbound

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Enhancing Model Interpretability with Local Attribution over Global Exploration

Reference 34

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Observation d52c7128-af61-4e03-8688-7897b934e3d8 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 35

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration Towards evaluating the robustness of neural networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.294216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.682898Z digest=sha256:ff8a742444622692de546267198661b65d3e5363683b180b830d7cf547554678

Observation 5a854719-6ea4-4674-bcb1-a4f51cc27af0 · outbound

This paper cites Benchmarking transferable adversarial attacks.,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Benchmarking transferable adversarial attacks.,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.277762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.693518Z digest=sha256:6abb1e73f43ae6b29384b369726a3a4ce3a9d2d3b8a866fcbc69116c591f7cfa

Observation 73632eb1-a9f3-4fe5-9270-ba2eeaac66fc · outbound

This paper cites Boost- ing adversarial attacks with momentum,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Boost- ing adversarial attacks with momentum,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.257255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.698098Z digest=sha256:dee4dfa79a9b6600f57a70c6db984e060b88c6a0b12c20e4821c0066251bc475

Observation f0c857fb-db92-45f5-81c0-fe38bb69db9a · outbound

This paper cites Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.240609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.703222Z digest=sha256:efc83eb97dd11b5cd2d1ab71a0cfeff655591232e5ff03e2f9bf9dd7411f036c

Observation 9fbed891-a686-4310-951a-0c8d8e4fd604 · outbound

This paper cites Improving transferability of adversarial examples with input diversity,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Improving transferability of adversarial examples with input diversity,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.220701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.707914Z digest=sha256:ba3eb45bd6282dc35cefbd04542ed543a3e5dd425beeca3ea3342a4f1f627014

Observation 9ab00d03-a491-4b82-bd1c-224fb6a580a8 · outbound

This paper cites Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.712400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.712400Z digest=sha256:16ffb770f57bfd17e82cbd64ac170f8ce90d9039eeae11288e6e43460321e777

Observation 18221b05-3faa-4ff5-bda0-1f9c0551d86b · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Evading defenses to transferable adversarial examples by translation-invariant attacks,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.202140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.717423Z digest=sha256:78069b1db0ff97b0a109730a01f5ed44a8e03ea3d1d38dfd9de73143583a880e

Observation c0162563-c057-4a0b-a792-648ac8ba4f1b · outbound

This paper cites Improving adversarial transferability via neuron attribution-based attacks,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Improving adversarial transferability via neuron attribution-based attacks,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.185900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.722260Z digest=sha256:29abd8212c7462d9ed26cad54bd2b0a89c2e36cdb793c72f3ac1d070de08daf4

Observation 656613ce-8895-415a-9e9c-d4957576e99a · outbound

This paper cites Structure invariant transformation for better adversarial transferability,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Structure invariant transformation for better adversarial transferability,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.170845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.727565Z digest=sha256:594d2fee77046e42980227bbd800c243bd7925199c864bcb4db422957cc4a40b

Observation 02bb85cb-c6cd-4173-92b3-f5b7190d265f · outbound

This paper cites Boosting adversarial transferability via gradient relevance attack,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Boosting adversarial transferability via gradient relevance attack,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.155019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.732084Z digest=sha256:78d244704c88a7dc336a0428d9ecbaa35a7b4d6fcd576239c3db8a036a3bd51f

Observation 77c60702-5cbe-495e-9c9b-d06287fc48e8 · outbound

This paper cites Improving adversarial transferability via frequency-based stationary point search,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Improving adversarial transferability via frequency-based stationary point search,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.138024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.736697Z digest=sha256:a3d1400fe863332a4ed8d1e79b3060487f0ad7e73e393fc05efffe7faafffb69

Observation 2738f06b-7d82-4e95-823d-36de8209845d · outbound

This paper cites Adversarial examples in the physical world,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Adversarial examples in the physical world,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.741774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.741774Z digest=sha256:db502b88f1ab836af810bcd841c10d7e3e5363f74e1023627fdb64e48b3a93ed

Observation 8a7549f6-616c-4779-8eed-2863b24a53de · outbound

This paper cites Frequency domain model augmentation for adversarial attack,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Frequency domain model augmentation for adversarial attack,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:19:49.105512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T00:19:48.746315Z digest=sha256:127d89fc67f58c4b1588aedc555bc68cb93162978a66af9ea9c83430c75bda07

Observation c2b1e97b-51c3-40b2-b613-05f6723f371b · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Imagenet: A large-scale hierarchical image database,

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.751303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.751303Z digest=sha256:903887fc947fa80250c97680dbea7d4ebea24d0241233654eb7d94bdf14d492e

Observation 52ad8a5b-2636-438c-88d7-6611810e4df2 · outbound

This paper cites Rethinking the inception architecture for computer vision,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Rethinking the inception architecture for computer vision,

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.756636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.756636Z digest=sha256:e89d090370cc93e0dcc0f213487fc26d48c7827bdeb98461520d0d582d23dd48

Observation 5f3eccc3-8a92-412f-94f7-d71dfab2be30 · outbound

This paper cites Deep residual learning for image recognition,.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Deep residual learning for image recognition,

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.764151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.764151Z digest=sha256:c39cb0a474d862eb62b87bc6fb67751e801b2c80f35246459505f951334c2a4b

Observation 195f7407-3f3b-4024-98c1-3746f886e4ef · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.770345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.770345Z digest=sha256:ce92c87889f0c1a52c821f253aa3d605c15d8390a56981996bab061f796cf44a

Observation b0dd81bb-cc23-4f79-9bfc-4419fafd430e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.775336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.775336Z digest=sha256:8ecbde5d952d44a71a03b55aad57c997ff52c98236d1edb7ce1b5a375089fee4

Observation 5dbc0804-179f-47dd-b2b8-00fa4c389154 · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-11T00:19:48.780152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:19:48.780152Z digest=sha256:f58147993997ffc1f227868bbaf887146efeb731b5278baecca03adc082bed63

Pith citing papers

No inbound Pith citation observations are available.