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Paper Citation Record · LEDGER

Err on the Side of Texture: Texture Bias on Real Data

As of 17 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2412.10597.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.10597 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:52:26.632555Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

  • verified exact6
  • verified fuzzy10
  • unresolved32
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c33b7e7-d82a-460e-92a1-64a3cbb6aaf5 · outbound

This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,.

Err on the Side of Texture: Texture Bias on Real Data ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.337977Z digest=sha256:b9706ad02a6a7daf3206c542a916c0a47ad2bd8ed0ca0521e423b2bfafa80f1f

Observation 5c91dc5e-0eae-4d51-bdb9-e2379e1dc862 · outbound

This paper cites On the Performance of GoogLeNet and AlexNet Applied to Sketches,.

Err on the Side of Texture: Texture Bias on Real Data On the Performance of GoogLeNet and AlexNet Applied to Sketches,

Reference 2

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source=pdf_text observed=2026-08-11T15:52:26.350551Z digest=sha256:4b6d52c9577dec1389b19993fc9f9b5036874d83501fdc2aa1a605f97dd98c08

Observation 4329f7f8-6543-4384-9c2d-10d9066ad2a6 · outbound

This paper cites Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet.

Err on the Side of Texture: Texture Bias on Real Data Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Reference 3

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source=pdf_text observed=2026-08-11T15:52:26.356861Z digest=sha256:c0a67a6ee7d5da2bd1f559ee845792679a8dc928581955bb4f7604628b5cc058

Observation c59d3b69-846e-4b35-82c4-c3972680ab8d · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-11T15:52:26.363560Z digest=sha256:5d53f85869cda3dcbf69edcdd02644299b55cd3b27a637f0c3d09b0478b2db68

Observation e110e125-20c8-43be-9ecc-9a46756518b0 · outbound

This paper cites Generalisation in humans and deep neural networks.

Err on the Side of Texture: Texture Bias on Real Data Generalisation in humans and deep neural networks

Reference 5

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source=pdf_text observed=2026-08-11T15:52:26.370323Z digest=sha256:d5735ab5ef7e1fff7bd2ce269d516619fff9569e73bd764dcc8d892d51507fc7

Observation 89a206f6-640a-4b85-ae2a-fb4644bf6ca2 · outbound

This paper cites Natural Adversarial Examples.

Err on the Side of Texture: Texture Bias on Real Data Natural Adversarial Examples

Reference 6

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source=pdf_text observed=2026-08-11T15:52:26.378581Z digest=sha256:7754d5bfebf24ea6090d30ba7ff6234fcc97cc166d57b1e4b5e43ef3ea1caac1

Observation 5c0abc14-7441-4b7a-8683-4b0abb2711ea · outbound

This paper cites On Synthetic Texture Datasets: Challenges, Creation, and Curation.

Err on the Side of Texture: Texture Bias on Real Data On Synthetic Texture Datasets: Challenges, Creation, and Curation

Reference 7

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verified exact
local_arxiv, observed 2026-08-11T15:52:27.068505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.385689Z digest=sha256:c815c8f3fbab8f1f06a2c0a413915c29e2684b94eb81b28e1eb654a2e001ce20

Observation af57db43-fdf5-49fb-8191-8055f3d40a4f · outbound

This paper cites Explorations in Texture Learning.

Err on the Side of Texture: Texture Bias on Real Data Explorations in Texture Learning

Reference 8

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local_arxiv, observed 2026-08-11T15:52:27.030994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.392512Z digest=sha256:b4f19493d5eaf0d958156f4c28aa2c36421e7629525bd4ac5782c265c60270dd

Observation a40632bb-516b-4a2b-9933-d3bdd13ab210 · outbound

This paper cites Describing Textures in the Wild,.

Err on the Side of Texture: Texture Bias on Real Data Describing Textures in the Wild,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.398159Z digest=sha256:176e5ca6b9bc9137982163056bf910e9e910368ff1ffa1a5b4f7804c34345df7

Observation d15cb385-ba2f-4188-872f-2b8f00c606e4 · outbound

This paper cites Shortcut Learning in Deep Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Shortcut Learning in Deep Neural Networks

Reference 10

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source=pdf_text observed=2026-08-11T15:52:26.405398Z digest=sha256:6abb458a2a68cb3bdbd5ac71974520807a1cf806b490288d681c0bcc50a03b41

Observation 75a241e4-7543-4a9d-a02b-e5f8a3ef7987 · outbound

This paper cites Intriguing properties of neural networks.

Err on the Side of Texture: Texture Bias on Real Data Intriguing properties of neural networks

Reference 11

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source=pdf_text observed=2026-08-11T15:52:26.422659Z digest=sha256:38b141bb5513b2b5d5f9efb073cf9eeded51f7f2108820da5e1828cf16b95d4d

Observation b77dbddb-40e6-4a46-a0aa-f9a2d41a481c · outbound

This paper cites Evasion Attacks against Machine Learning at Test Time,.

Err on the Side of Texture: Texture Bias on Real Data Evasion Attacks against Machine Learning at Test Time,

Reference 12

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doi, observed 2026-08-11T15:52:26.980564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.428605Z digest=sha256:c2592143ca21fbea52f0d6ff70cfcb3cd44ec53dbae8d8e6332d63de42640251

Observation 6a0b02af-5b34-46b3-9b33-d9bdbb39857f · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Err on the Side of Texture: Texture Bias on Real Data Explaining and Harnessing Adversarial Examples

Reference 13

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source=pdf_text observed=2026-08-11T15:52:26.440555Z digest=sha256:070b937e38be17270455af5f4eda1f63ef94d5b968421c44654e7d93f0767bd4

Observation 7e5882e6-3728-4095-a42d-5670b0e1ce33 · outbound

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

Err on the Side of Texture: Texture Bias on Real Data Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 14

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source=pdf_text observed=2026-08-11T15:52:26.444982Z digest=sha256:7277b2af5d26798d08ac3299ba2cc96fc5c7f878d1ec7f893b611bd40aa2254f

Observation ec6fb53f-f122-4aa4-9edf-d1e5bd5a6a27 · outbound

This paper cites DeepFool: a simple and accurate method to fool deep neural networks.

Err on the Side of Texture: Texture Bias on Real Data DeepFool: a simple and accurate method to fool deep neural networks

Reference 15

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source=pdf_text observed=2026-08-11T15:52:26.451693Z digest=sha256:1c42497bf0be64b1c6fd8275eb1caa609f3afe5499a74c633db1400bd57ebd2d

Observation be6d8316-915d-46b7-abcb-2b544e54077a · outbound

This paper cites The Space of Adversarial Strategies.

Err on the Side of Texture: Texture Bias on Real Data The Space of Adversarial Strategies

Reference 16

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local_arxiv, observed 2026-08-11T15:52:27.850283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.461891Z digest=sha256:d06fa6229eca9102bf8ab2ba08c952cacf023e9e26c7130975b8bd40251ed96e

Observation 91f74a3d-40f8-47dd-bcf9-5a2b45878f17 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Towards Evaluating the Robustness of Neural Networks

Reference 17

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source=pdf_text observed=2026-08-11T15:52:26.468603Z digest=sha256:65d8093ac5f64cfdc1f2e5d50e88f726bec2b72a047a8ef56909078dfc62d70e

Observation 04d96936-4dd1-4f02-aaae-cb3846c96023 · outbound

This paper cites The Limitations of Deep Learning in Adversarial Settings.

Err on the Side of Texture: Texture Bias on Real Data The Limitations of Deep Learning in Adversarial Settings

Reference 18

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source=pdf_text observed=2026-08-11T15:52:26.474789Z digest=sha256:682c999b65e9fe2a70a6afe446bce8252a545a0a6753f3c8d025fbcd7d5f63fa

Observation 94f28d4f-5e3b-40c1-8234-4ac7b5547a08 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Err on the Side of Texture: Texture Bias on Real Data ImageNet Large Scale Visual Recognition Challenge

Reference 19

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source=pdf_text observed=2026-08-11T15:52:26.480448Z digest=sha256:c337213da6d3765433d233bd1985cb8e3e6373e2b8e1f031bafc4fed0d46a51c

Observation fefdb380-2ec0-4092-aea6-3e353067d4a2 · outbound

This paper cites Torchvision the machine- vision package of torch,.

Err on the Side of Texture: Texture Bias on Real Data Torchvision the machine- vision package of torch,

Reference 20

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source=pdf_text observed=2026-08-11T15:52:26.485651Z digest=sha256:fe395559cb109b7c5de894d831a90bd50661dc7af538706669ae8d83c14c1e7a

Observation 9b955127-eb42-441c-8cd4-5daa6ade0ac9 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Err on the Side of Texture: Texture Bias on Real Data Deep Residual Learning for Image Recognition

Reference 21

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source=pdf_text observed=2026-08-11T15:52:26.490658Z digest=sha256:d26f611d427bb9a8767b8bf200568012987d8c79d36b9f2386c7904ef173cff6

Observation ed0c1030-afde-479c-a0f6-6a92c6df4463 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 22

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source=pdf_text observed=2026-08-11T15:52:26.495913Z digest=sha256:9f005c22e79aca27b7b9719eb82a35b0e37c1f801554996031e53877e4e86590

Observation 594306ed-a5be-46fc-8dcb-7b8fa966c688 · outbound

This paper cites Densely Connected Convolutional Networks.

Err on the Side of Texture: Texture Bias on Real Data Densely Connected Convolutional Networks

Reference 23

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source=pdf_text observed=2026-08-11T15:52:26.501289Z digest=sha256:a009843e14494f0eec51ed6062ea73e9642f4f571cadf07d9c341fc1fdb94178

Observation 31fe393d-23e2-4d7a-9bfd-689051c54aa5 · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

Err on the Side of Texture: Texture Bias on Real Data Rethinking the Inception Architecture for Computer Vision

Reference 24

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source=pdf_text observed=2026-08-11T15:52:26.506203Z digest=sha256:493c293a8f79086ff51903b711cfcb05dd4835c627155c0b7f2025ba8f9442eb

Observation 1185e7bf-2189-4860-8b68-38bc8acd6580 · outbound

This paper cites Evasion Attacks against Machine Learning at Test Time.

Err on the Side of Texture: Texture Bias on Real Data Evasion Attacks against Machine Learning at Test Time

Reference 25

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source=pdf_text observed=2026-08-11T15:52:26.434511Z digest=sha256:32e9a3f0231461ef57c3db9457f0d13cfd7901fbd62f3307b2ab5aff65f98bdc

Observation ddcc4713-9ca9-4e3b-ad48-0b34037a9002 · outbound

This paper cites The Origins and Prevalence of Texture Bias in Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

Reference 26

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source=pdf_text observed=2026-08-11T15:52:26.518545Z digest=sha256:a8a00e98e08dab9e3eab8b3e9c61e71219463f505b14ab795163e102e89853f2

Observation fe9fa969-caf9-499f-993b-3977a3980de6 · outbound

This paper cites A ConvNet for the 2020s.

Err on the Side of Texture: Texture Bias on Real Data A ConvNet for the 2020s

Reference 27

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source=pdf_text observed=2026-08-11T15:52:26.512752Z digest=sha256:b498fb989f777610834436daa417bc96b334e8f45acb63736d0a433ebf4557f7

Observation 18fd791d-34c7-46b8-b821-54d328646a67 · outbound

This paper cites Texture Synthesis Using Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Texture Synthesis Using Convolutional Neural Networks

Reference 28

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source=pdf_text observed=2026-08-11T15:52:26.528372Z digest=sha256:53cbf8c0ae5b56c39f4dadc7105e9069ce906017a95cc8a1fb9f7e1be8d0b95a

Observation 6361632f-3217-4de5-b103-5ecf4da375be · outbound

This paper cites Network Dissection: Quantifying Interpretability of Deep Visual Representations,.

Err on the Side of Texture: Texture Bias on Real Data Network Dissection: Quantifying Interpretability of Deep Visual Representations,

Reference 29

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source=pdf_text observed=2026-08-11T15:52:26.523437Z digest=sha256:9788990101055854c5a0a8bc634c2bd3d09792648e5bfc671264066fa9e39ccc

Observation 0eb8b3b3-1459-4d26-bc10-81e885d4dcfb · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Err on the Side of Texture: Texture Bias on Real Data Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.537998Z digest=sha256:e5b0d228564276b981491b9e101578410abd094d2f84f77735a818711993c847

Observation bff59440-2147-4e75-bd1b-259e787c3781 · outbound

This paper cites Image Style Transfer Using Convolutional Neural Networks,.

Err on the Side of Texture: Texture Bias on Real Data Image Style Transfer Using Convolutional Neural Networks,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.533581Z digest=sha256:c4c51516e2dfa4435eaeb3d8729ceb41c6fdbe5499171ca0c74adf31c56b734a

Observation 2092ba5e-b3fb-4de2-80cf-4b73c318b8d8 · outbound

This paper cites Shape-Texture Debiased Neural Network Training,.

Err on the Side of Texture: Texture Bias on Real Data Shape-Texture Debiased Neural Network Training,

Reference 32

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raw_fallback, observed 2026-08-11T15:52:28.274082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.551498Z digest=sha256:13349730d7af8e78e2c2606954e3f8428decc8eb59d561fe15870983b84c8ffd

Observation 8d6b10f3-5998-487b-917d-b21088b19e22 · outbound

This paper cites Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition,.

Err on the Side of Texture: Texture Bias on Real Data Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition,

Reference 33

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source=pdf_text observed=2026-08-11T15:52:26.542697Z digest=sha256:bc50586bb62abba3db28dcc0c7cee9003ea9530aa030033761b4d94a6e8bc76f

Observation 9a5dc4ef-778e-4e32-9b48-cfcf18488b35 · outbound

This paper cites Adversarial Machine Learning at Scale,.

Err on the Side of Texture: Texture Bias on Real Data Adversarial Machine Learning at Scale,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.561532Z digest=sha256:814d9c5646d04d76682b97d042807eaecfd2b72a5de0c1c72fb7c62961077d4a

Observation 4754d572-cb5c-45ba-96c5-a3cbd2b471b3 · outbound

This paper cites Learning with a Strong Adversary.

Err on the Side of Texture: Texture Bias on Real Data Learning with a Strong Adversary

Reference 35

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source=pdf_text observed=2026-08-11T15:52:26.556374Z digest=sha256:8562ac921c9ebefbba6f61c6798d0274d9923605200a42af62a3e26fb616f1bf

Observation ccdef954-7e9c-435b-b175-bd770bcef987 · outbound

This paper cites A Neural Algorithm of Artistic Style.

Err on the Side of Texture: Texture Bias on Real Data A Neural Algorithm of Artistic Style

Reference 36

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source=pdf_text observed=2026-08-11T15:52:26.578368Z digest=sha256:8b73dc5bf9eb66e35686ff0cfb51f186940c5cb185e42804acbc2228ff36d3a7

Observation 3214496b-2ba9-4195-8168-8e225b2ef522 · outbound

This paper cites Deep Learning based Feature Ex- traction for Texture Classification,.

Err on the Side of Texture: Texture Bias on Real Data Deep Learning based Feature Ex- traction for Texture Classification,

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.583830Z digest=sha256:80040efc7652b70ab89e1d1cf85e2dfad29aba221126c27e9781ecee2f14550b

Observation 91019e4c-bc43-4e1a-8b6f-52ffebc7f0a5 · outbound

This paper cites Interpreting Adversarially Trained Convolutional Neural Networks,.

Err on the Side of Texture: Texture Bias on Real Data Interpreting Adversarially Trained Convolutional Neural Networks,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.223732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.573517Z digest=sha256:802354bff89aae8801362d8239bfa7acc0720df5c0791e3f1e2abecaf59986e2

Observation c937c8a8-ca86-408a-a666-f146cba31b83 · outbound

This paper cites Explore the Transfor- mation Space for Adversarial Images,.

Err on the Side of Texture: Texture Bias on Real Data Explore the Transfor- mation Space for Adversarial Images,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.593136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.593136Z digest=sha256:0139698b866fcc2c529abada0d92e50897a03e49067cf576c3d7aa84348bb179

Observation a7f679b4-eb63-4416-8a3e-5171f64c9092 · outbound

This paper cites Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks.

Err on the Side of Texture: Texture Bias on Real Data Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:52:26.705744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.598772Z digest=sha256:1b01dbb58889b497c07ecf42c554d05a712e43a32d9b38db9edcd2fd20fc94ed

Observation 8e79487c-0582-4ecf-913a-a72631b9ea8d · outbound

This paper cites Color encoding in biologically-inspired convolutional neural networks,.

Err on the Side of Texture: Texture Bias on Real Data Color encoding in biologically-inspired convolutional neural networks,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.169889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.588636Z digest=sha256:50196db56070e576a903226697617595961149b0cc657cabed731117c201e856

Observation 84eb2037-085b-4401-be70-3a16eb5689f9 · outbound

This paper cites Universal adversarial perturbations.

Err on the Side of Texture: Texture Bias on Real Data Universal adversarial perturbations

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.604664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.604664Z digest=sha256:91c03ced71e23aca6a567cc1643a01bff0ba0a257e00f5849379664e9602c841

Observation 6eb77072-e224-4f3e-918d-73528c645dd9 · outbound

This paper cites These files contain the necessary instructions, images, and the script you will run for this study.

Err on the Side of Texture: Texture Bias on Real Data These files contain the necessary instructions, images, and the script you will run for this study

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.144770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.609588Z digest=sha256:fb82beea1b1faa1b3581e72b4363cdcaf9b6e7870aac1dd1c954f0388aa59f25

Observation 9f4389e0-ca44-4906-b48e-fad30f24bde9 · outbound

This paper cites python3 eval_packages.py package_num The script will display 100 images, one at a time in a pop-up window along with four words in the terminal.

Err on the Side of Texture: Texture Bias on Real Data python3 eval_packages.py package_num The script will display 100 images, one at a time in a pop-up window along with four words in the terminal

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.126106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.614679Z digest=sha256:5acaab385fc12464d59f57494cd6b7c12bf172c4bc58bd54ef547a57b39ad759

Observation 7aa95d8e-6622-4fdd-82f9-398145d45cea · outbound

This paper cites Your task is to input the number corre- sponding to the texture that you believe is most prominent in the image.

Err on the Side of Texture: Texture Bias on Real Data Your task is to input the number corre- sponding to the texture that you believe is most prominent in the image

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.107129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.620617Z digest=sha256:6f8b046dc236e469da9b52e6ce6f57da83cb38f98a08cc150f327582facc9bc5

Observation d5ca3f70-e403-4917-be0f-1180dbf7ba07 · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:52:28.083175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.626592Z digest=sha256:cee0810e448910ef40f74c831b0b5e4336bc75d1712dc01de94dfcec57febc68

Observation 219a4dcb-9dfd-4b9a-9ffc-95b06d8b817a · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:52:28.057522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.632555Z digest=sha256:7e3c364559269cb373f9d0c528ebb5723a2dff2bfa7ce89854a201209553fa29

Observation b4f2d358-8757-419c-a22f-0b5ca8066960 · outbound

This paper cites Adversarial Machine Learning at Scale.

Err on the Side of Texture: Texture Bias on Real Data Adversarial Machine Learning at Scale

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.567946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.567946Z digest=sha256:d0e8f18d96872d762522b34adb9716874a13af7418f70fe9c871cc09c1975b37

Observation 05e4e3b2-0280-45a3-b61c-e827ebbdac92 · outbound

This paper cites Available: http://arxiv.org/abs/1811.

Err on the Side of Texture: Texture Bias on Real Data Available: http://arxiv.org/abs/1811

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.303749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T15:52:26.345199Z digest=sha256:68f68417b59c87fb3432e917b9bc6d7e7ba8c1cf6c8d6f22e41e6c00e10fe19c

Pith citing papers

No inbound Pith citation observations are available.