Paper Citation Record · LEDGER

Random Directional Attack for Fooling Deep Neural Networks

As of 16 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.02658.

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

pith.paper-citation-record.v1
1908.02658 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:03:46.865380Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

51 of 51 outbound references displayed

verified exact3
verified fuzzy17
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4dd05b81-4a02-46bc-926b-f1fc7dd85bd6 · outbound

This paper cites Image denoising and inpainting with deep neural networks,.

Random Directional Attack for Fooling Deep Neural Networks Image denoising and inpainting with deep neural networks,

Reference 1

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T15:03:47.981766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e93ae069-e902-4651-986b-1cef68561dde · outbound

This paper cites Context encoders: Feature learning by inpainting,.

Random Directional Attack for Fooling Deep Neural Networks Context encoders: Feature learning by inpainting,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.961557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2f0c57b0-4d36-4871-b406-3daabaa3eaa8 · outbound

This paper cites A uniﬁed architecture for natural language processing: Deep neural networks with multitask learning,.

Random Directional Attack for Fooling Deep Neural Networks A uniﬁed architecture for natural language processing: Deep neural networks with multitask learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.943522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f29dc466-82ba-45e5-b602-c60bb33b0ebc · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition,.

Random Directional Attack for Fooling Deep Neural Networks Deep neural networks for acoustic modeling in speech recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.924557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 526ba6e0-0087-4fbf-9a7f-92ca9a3dd617 · outbound

This paper cites Intriguing properties of neural networks.

Random Directional Attack for Fooling Deep Neural Networks Intriguing properties of neural networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.526949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc98155f-07e5-46b6-8fae-4c57a7fa51fa · outbound

This paper cites Univer- sal adversarial perturbations,.

Random Directional Attack for Fooling Deep Neural Networks Univer- sal adversarial perturbations,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.533980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5cc5032-8154-4942-8efc-c0f890935c9d · outbound

This paper cites Synthesizing Robust Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks Synthesizing Robust Adversarial Examples

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.545496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71693732-9dcc-4916-989e-91f0f4a53ebe · outbound

This paper cites Adversarial examples for semantic segmentation and object detection,.

Random Directional Attack for Fooling Deep Neural Networks Adversarial examples for semantic segmentation and object detection,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.553575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaca42b8-3fcf-4f00-ae3c-5521a98278bb · outbound

This paper cites Audio adversarial examples: Targeted attacks on speech-to-text,.

Random Directional Attack for Fooling Deep Neural Networks Audio adversarial examples: Targeted attacks on speech-to-text,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.882116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 740291a0-5ea8-4d57-a46b-da69ba87a51e · outbound

This paper cites Did you hear that? Adversarial Examples Against Automatic Speech Recognition.

Random Directional Attack for Fooling Deep Neural Networks Did you hear that? Adversarial Examples Against Automatic Speech Recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.570378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae4e5e30-7853-455b-93be-71cba0102763 · outbound

This paper cites HotFlip: White-Box Adversarial Examples for Text Classification.

Random Directional Attack for Fooling Deep Neural Networks HotFlip: White-Box Adversarial Examples for Text Classification

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.582216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40e38f4b-0e2c-4d02-ace6-56ec61545c99 · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: A survey,.

Random Directional Attack for Fooling Deep Neural Networks Threat of adversarial attacks on deep learning in computer vision: A survey,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.589726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9daaae66-cca7-4208-a3bf-c17a9705ad6a · outbound

This paper cites Adversarial Machine Learning at Scale.

Random Directional Attack for Fooling Deep Neural Networks Adversarial Machine Learning at Scale

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.603083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbfc635c-8209-44b9-8677-733db753cbc9 · outbound

This paper cites Boosting adversarial attacks with momentum,.

Random Directional Attack for Fooling Deep Neural Networks Boosting adversarial attacks with momentum,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.610775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72ad3b74-e15a-425b-a68f-5936b065cf3e · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks Explaining and Harnessing Adversarial Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.616417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07ea3842-83b8-462a-b1fa-dda0682665af · outbound

This paper cites A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.622852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596fb11b-07d0-419f-ad82-9261bb311dbc · outbound

This paper cites Towards Deep Neural Network Architectures Robust to Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks Towards Deep Neural Network Architectures Robust to Adversarial Examples

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.629357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e0842fa-4de8-49a8-81d0-8fad2a6ec8ad · outbound

This paper cites Exploring the space of adversarial images,.

Random Directional Attack for Fooling Deep Neural Networks Exploring the space of adversarial images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.836347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0df42a72-f77d-4d4c-a5fb-c1dc108f74f3 · outbound

This paper cites Intriguing Properties of Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks Intriguing Properties of Adversarial Examples

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.644451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c897984c-430f-431d-81d1-47086abb0cd4 · outbound

This paper cites Adver- sarially robust generalization requires more data,.

Random Directional Attack for Fooling Deep Neural Networks Adver- sarially robust generalization requires more data,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.813664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c1eccf7-8d14-41dd-986d-b613acb0ff11 · outbound

This paper cites Adversarial examples from computational constraints.

Random Directional Attack for Fooling Deep Neural Networks Adversarial examples from computational constraints

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:03:47.206476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 19356489-4b5d-4c3f-bd3b-f09ae74ebbd7 · outbound

This paper cites Adversarial Examples Are Not Bugs, They Are Features.

Random Directional Attack for Fooling Deep Neural Networks Adversarial Examples Are Not Bugs, They Are Features

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.667194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1566e90-f577-4d0a-aacf-984d66dbc72a · outbound

This paper cites Disentangling adversarial robust- ness and generalization,.

Random Directional Attack for Fooling Deep Neural Networks Disentangling adversarial robust- ness and generalization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.795094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13ec7ec7-3ece-437c-9121-1a8a448be04f · outbound

This paper cites Adversarial examples in the physical world.

Random Directional Attack for Fooling Deep Neural Networks Adversarial examples in the physical world

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.681071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a46f3a2a-7437-4085-a12c-6e89cb5baaa6 · outbound

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

Random Directional Attack for Fooling Deep Neural Networks Deepfool: a simple and accurate method to fool deep neural networks,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.691218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52b5d33d-3d2e-4888-ba5e-54831f19f17c · outbound

This paper cites The limitations of deep learning in adversarial settings,.

Random Directional Attack for Fooling Deep Neural Networks The limitations of deep learning in adversarial settings,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.698199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55d2ed3-87bd-4ee8-b4da-4056fd611585 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Random Directional Attack for Fooling Deep Neural Networks Towards evaluating the robustness of neural networks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.704948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c954a21-4b40-40fd-ac52-defd5596fae4 · outbound

This paper cites One pixel attack for fooling deep neural networks,.

Random Directional Attack for Fooling Deep Neural Networks One pixel attack for fooling deep neural networks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.710455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 544dfa6f-f896-4714-9fdc-573ad2fea5be · outbound

This paper cites Ead: elastic- net attacks to deep neural networks via adversarial examples,.

Random Directional Attack for Fooling Deep Neural Networks Ead: elastic- net attacks to deep neural networks via adversarial examples,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.725790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48b37ac3-188d-47d0-83bb-f54481e9febf · outbound

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

Random Directional Attack for Fooling Deep Neural Networks Improving transferability of adversarial examples with input diversity,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.706418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44f3bfde-8268-45a0-8ad0-3c8e3f6aed8e · outbound

This paper cites Semantic adversarial examples,.

Random Directional Attack for Fooling Deep Neural Networks Semantic adversarial examples,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.685121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e11e16f8-b83f-4c20-ae08-9a52535f84f8 · outbound

This paper cites Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:03:47.132104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 155a8e3d-564d-47f5-99fb-c69493ce8057 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Random Directional Attack for Fooling Deep Neural Networks Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.737003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d96c4027-7118-462c-9b3c-fc65557c9ab9 · outbound

This paper cites Ensemble selection from libraries of models,.

Random Directional Attack for Fooling Deep Neural Networks Ensemble selection from libraries of models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.652845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c8f9ad6-90ea-4412-806c-8ecef70e8fd9 · outbound

This paper cites Enhancing Robustness of Machine Learning Systems via Data Transformations.

Random Directional Attack for Fooling Deep Neural Networks Enhancing Robustness of Machine Learning Systems via Data Transformations

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.748271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aed0a8b9-3664-47f0-92d8-4c43e78e7618 · outbound

This paper cites Thermometer encoding: One hot way to resist adversarial examples,.

Random Directional Attack for Fooling Deep Neural Networks Thermometer encoding: One hot way to resist adversarial examples,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.628122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 126517ce-fd97-41c7-a5a4-cf662462746f · outbound

This paper cites Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.

Random Directional Attack for Fooling Deep Neural Networks Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.760997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a818c1e-987c-437f-820b-379f5cd53b50 · outbound

This paper cites Comdefend: An efﬁcient image compression model to defend adversarial examples,.

Random Directional Attack for Fooling Deep Neural Networks Comdefend: An efﬁcient image compression model to defend adversarial examples,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.602052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9587b16c-764b-4310-a503-b7c04ddd9f19 · outbound

This paper cites Divide, Denoise, and Defend against Adversarial Attacks.

Random Directional Attack for Fooling Deep Neural Networks Divide, Denoise, and Defend against Adversarial Attacks

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:03:47.046951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0824c01-abfc-4bf1-8a95-895da7d9cab9 · outbound

This paper cites Image blind denoising with generative adversarial network based noise modeling,.

Random Directional Attack for Fooling Deep Neural Networks Image blind denoising with generative adversarial network based noise modeling,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.780958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4543717b-dd00-429a-8963-77a368193936 · outbound

This paper cites Deﬂecting adversarial attacks with pixel deﬂection,.

Random Directional Attack for Fooling Deep Neural Networks Deﬂecting adversarial attacks with pixel deﬂection,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.559767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c0738cc-8b47-4328-bcaf-ca52cd5fb91a · outbound

This paper cites Detecting Adversarial Samples from Artifacts.

Random Directional Attack for Fooling Deep Neural Networks Detecting Adversarial Samples from Artifacts

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.792404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de1c042-b226-4ce3-af56-b65575bafe2d · outbound

This paper cites On the (Statistical) Detection of Adversarial Examples.

Random Directional Attack for Fooling Deep Neural Networks On the (Statistical) Detection of Adversarial Examples

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.802243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de192b60-160e-4895-bf0d-beb018382287 · outbound

This paper cites Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality.

Random Directional Attack for Fooling Deep Neural Networks Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.811501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98bf8a64-d545-40e8-ad09-1ddca572178b · outbound

This paper cites Safetynet: Detecting and rejecting adversarial examples robustly,.

Random Directional Attack for Fooling Deep Neural Networks Safetynet: Detecting and rejecting adversarial examples robustly,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.542215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6a23e747-c6d8-4d8f-8dfd-f9d600ef6161 · outbound

This paper cites Detecting adversarial image examples in deep neural networks with adaptive noise reduction,.

Random Directional Attack for Fooling Deep Neural Networks Detecting adversarial image examples in deep neural networks with adaptive noise reduction,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.521388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a67eed5-02b4-407a-bc72-d8f5a10da7b2 · outbound

This paper cites A simple uniﬁed framework for detecting out-of-distribution samples and adversarial attacks,.

Random Directional Attack for Fooling Deep Neural Networks A simple uniﬁed framework for detecting out-of-distribution samples and adversarial attacks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:03:47.501929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44c2b949-915e-4605-a6ec-bd63bfb5fc62 · outbound

This paper cites an unresolved cited work.

Random Directional Attack for Fooling Deep Neural Networks Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:03:47.477771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 744734c5-e416-4eb5-a25c-dbbad0cbabd8 · outbound

This paper cites Optimization by simulated annealing,.

Random Directional Attack for Fooling Deep Neural Networks Optimization by simulated annealing,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.849082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7110b051-e513-4e6e-8bae-fb8ce7c703df · outbound

This paper cites an unresolved cited work.

Random Directional Attack for Fooling Deep Neural Networks Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:03:47.434069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32ec1baf-133d-460d-b1cb-e7c7537448d6 · outbound

This paper cites Technical Report on the CleverHans v2.1.0 Adversarial Examples Library.

Random Directional Attack for Fooling Deep Neural Networks Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-14T15:03:46.865380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.