Paper Citation Record · LEDGER

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.03765.

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

pith.paper-citation-record.v1
2506.03765 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:00:08.189686Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

40 of 40 outbound references displayed

verified exact0
verified fuzzy25
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7dda4e11-b6a8-4015-9864-07a6167b34cd · outbound

This paper cites Explaining and harnessing adver- sarial examples.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Explaining and harnessing adver- sarial examples

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:09.023509Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72f6c914-85c9-44d2-aaea-74cd24efd2d1 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Towards evaluating the robustness of neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.996530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c16475f-8ab8-40d7-bc1e-dbe8b9b7e1b0 · outbound

This paper cites Understanding adversarial attacks on deep learning based medical image analysis systems.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Understanding adversarial attacks on deep learning based medical image analysis systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.980686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c0d560e-22ae-46a4-ae1a-475c9509c036 · outbound

This paper cites an unresolved cited work.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:00:08.963884Z

Source-reported events for the cited work

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Observation 4c1da609-f2a6-4efe-8a97-ca51b328b54d · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Towards deep learning models resistant to adversarial attacks

Reference 5

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Source-reported events for the cited work

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Observation 67d1f0e4-57e5-4cbe-b1f6-0df00903b951 · outbound

This paper cites Fast is better than free: Revisiting adversarial training.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Fast is better than free: Revisiting adversarial training

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.937248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2505b408-81b5-48de-8727-6ac5f79819d8 · outbound

This paper cites A comprehensive study on robustness of image classification models: Benchmarking and rethinking.International Journal of Computer Vision, pages 1–23, 2024.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples A comprehensive study on robustness of image classification models: Benchmarking and rethinking.International Journal of Computer Vision, pages 1–23, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.917104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40153afd-862a-43e7-9b9f-7881f87cc035 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.898749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50c8d555-dcd1-4766-a924-7427714bbe5c · outbound

This paper cites Detecting adversarial data by probing multiple perturbations using expected perturbation score.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Detecting adversarial data by probing multiple perturbations using expected perturbation score

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.880556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ea7616f-3d23-4467-8527-c743faa3cbe4 · outbound

This paper cites Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.861131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 803b742f-a03b-43f2-b034-048650d1e5f6 · outbound

This paper cites Characterizing adversarial subspaces using local intrinsic dimensionality.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Characterizing adversarial subspaces using local intrinsic dimensionality

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.841786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a31434a4-9fd0-4342-8c65-a24d2e239a6b · outbound

This paper cites Detecting adversarial faces using only real face self-perturbations.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Detecting adversarial faces using only real face self-perturbations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.824414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02221bca-5e5a-4022-a0ba-fa871e6f2f4d · outbound

This paper cites Detecting adversarial examples through image transformation.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Detecting adversarial examples through image transformation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.806217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b948c1a4-87db-4423-aeca-f2dc31eb4754 · outbound

This paper cites Adver- sarial example detection for dnn models: A review and experimental comparison.Artificial Intelligence Review, pages 1–60, 2022.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Adver- sarial example detection for dnn models: A review and experimental comparison.Artificial Intelligence Review, pages 1–60, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.788536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67894547-a684-42fc-a905-18bc4fb9dfe8 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.771619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b709e36-8c57-4183-8a9b-acef2fd58ac6 · outbound

This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.755754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82e54c4c-081e-4ce2-9a46-d7ede4ee62dc · outbound

This paper cites Square at- tack: a query-efficient black-box adversarial attack via random search.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Square at- tack: a query-efficient black-box adversarial attack via random search

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.738616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a92459f4-f5fa-453c-a1f4-798b5ab9841e · outbound

This paper cites Adam: A method for stochastic optimization.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Adam: A method for stochastic optimization

Reference 18

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Source-reported events for the cited work

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Observation b2c5bbbb-ed16-4874-a41f-7b5ee311f8e3 · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Deepfool: a simple and accurate method to fool deep neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.709851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a2c677c-4561-4683-87d9-bf52606b2292 · outbound

This paper cites Triangle attack: A query-efficient decision-based adversarial attack.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Triangle attack: A query-efficient decision-based adversarial attack

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.691791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38ae3260-21e2-4dfe-b44a-31d23f60b074 · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Enhancing the transferability of adversarial attacks through variance tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.064720Z

Source-reported events for the cited work

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Observation 6e2182fe-5357-4d63-82a2-7a5a066e6313 · outbound

This paper cites Nesterov accelerated gradient and scale invariance for adversarial attacks.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Nesterov accelerated gradient and scale invariance for adversarial attacks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.656605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b09bd2c-63a3-432a-97ba-a6e23f18936d · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Score-based generative modeling through stochastic differential equations

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.079792Z

Source-reported events for the cited work

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Observation d3245452-2c01-4f29-b54f-4c201f12ff2d · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples An image is worth 16x16 words: Transformers for image recognition at scale

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.087132Z

Source-reported events for the cited work

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Observation 9fbd5944-2fa1-4ce8-a5bd-f7de4e597c8d · outbound

This paper cites Learning transferable visual models from natural language supervision.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Learning transferable visual models from natural language supervision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.092363Z

Source-reported events for the cited work

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Observation 279ead43-5077-41b6-8e1a-7f544c5c09b2 · outbound

This paper cites Improving fast adversarial training with prior-guided knowledge.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Improving fast adversarial training with prior-guided knowledge.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.584209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8093b998-03ef-4af6-99cb-a2d61b10bf9d · outbound

This paper cites On the robustness of vision trans- formers to adversarial examples.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples On the robustness of vision trans- formers to adversarial examples

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.562699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ff9daa6-5724-4439-ac0c-c3d94c36de6c · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Foundation models defining a new era in vision: a survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

Reference 28

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Source-reported events for the cited work

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Observation 17ccb9e9-c679-4f23-856b-3607cde82021 · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Learning multiple layers of features from tiny images

Reference 29

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Source-reported events for the cited work

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Observation d58fa7f0-95e0-455d-9c62-29a66f3ff07e · outbound

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

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Imagenet: A large- scale hierarchical image database

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.514343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96e5f109-d4da-480a-9dc4-b0c4df8fb515 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Very deep convolutional networks for large-scale image recognition

Reference 31

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Observation 62c8dd9a-fdba-4d8f-8c46-346f3a157644 · outbound

This paper cites Deep residual learning for image recognition.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Deep residual learning for image recognition

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.136042Z

Source-reported events for the cited work

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Observation a4141720-0a0e-4716-bfe3-57cc63f0824d · outbound

This paper cites Diffusion models for adversarial purification.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Diffusion models for adversarial purification

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.466689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 582ffa53-6919-495e-9360-98403bf967b7 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020

Reference 34

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Source-reported events for the cited work

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Observation f2cdffe2-e6ea-49ee-b861-85f3930c2a6d · outbound

This paper cites Robust models are less over-confident.Advances in Neural Information Processing Systems, 35:39059–39075, 2022.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Robust models are less over-confident.Advances in Neural Information Processing Systems, 35:39059–39075, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.435308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad425545-e183-4db7-90fd-377dd4bcf984 · outbound

This paper cites Evad- ing adversarial example detection defenses with orthogonal projected gradient descent.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Evad- ing adversarial example detection defenses with orthogonal projected gradient descent

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.411780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da3adc30-68f0-4cd7-b923-cb56051ef91b · outbound

This paper cites A convnet for the 2020s.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples A convnet for the 2020s

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.170202Z

Source-reported events for the cited work

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Observation 5d43278b-2bef-4578-9fa2-5ee947091ea9 · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:08.176290Z

Source-reported events for the cited work

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Observation d1ed238a-e32f-4499-a136-cb6365563619 · outbound

This paper cites Benchmarking adversarial robustness on image classification.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Benchmarking adversarial robustness on image classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:00:08.378548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec239471-69a3-4f5d-8549-01f2717ee8b2 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Diffusion models beat gans on image synthesis

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T11:00:08.332773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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