Paper Citation Record · LEDGER

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve

As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:1908.01469.

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

pith.paper-citation-record.v1
1908.01469 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:15:45.680437Z

measured 21 of 21 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

21 of 21 outbound references displayed

verified exact0
verified fuzzy12
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 705fbfa9-f980-4bb3-8ec2-e401eee1c315 · outbound

This paper cites In: Joint European conference on machine learning and knowledge discovery in databases.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Joint European conference on machine learning and knowledge discovery in databases

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.953991Z

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.

Observation b5d4d1ea-ec1b-495b-8800-dacac266a624 · outbound

This paper cites In: 2017 IEEE Symposium on Security and Privacy (SP).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: 2017 IEEE Symposium on Security and Privacy (SP)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.596162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c840428-47b2-453c-a2c8-0cf577d0caa7 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve Explaining and Harnessing Adversarial Examples

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.601255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0c48d0f-284a-47e4-a705-946591d617b7 · outbound

This paper cites In: European Symposium on Research in Com- puter Security.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: European Symposium on Research in Com- puter Security

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.932892Z

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.

Observation a35672e6-1ba4-45d7-88ba-fc4a19befd7c · outbound

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

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve Towards Deep Neural Network Architectures Robust to Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.610020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8657e542-438a-47e1-8726-921fb31c1a8a · outbound

This paper cites In: Proceedings of the 4th ACM workshop on Security and artiﬁcial intelligence.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Proceedings of the 4th ACM workshop on Security and artiﬁcial intelligence

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.920642Z

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.

Observation f527da40-9cab-45b5-bf1a-51d5f97b7a28 · outbound

This paper cites In: International Conference on Learning Representations ICLR (2018).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: International Conference on Learning Representations ICLR (2018)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.908399Z

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.

Observation a23e1b54-1f10-44e5-a0d8-dd4321692f1b · outbound

This paper cites Adversarial Machine Learning at Scale.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve Adversarial Machine Learning at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.622696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d0fa1d-3906-49b9-917f-aa6afccd15fa · outbound

This paper cites nature 521(7553), 436 (2015) Automated Detection System for Adversarial Examples 15.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve nature 521(7553), 436 (2015) Automated Detection System for Adversarial Examples 15

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.895312Z

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.

Observation 79ad4e0e-a619-47b6-90cb-651583395bdd · outbound

This paper cites AT&T Labs [Online].

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve AT&T Labs [Online]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.882856Z

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.

Observation 4d4961a8-89c9-4231-b48b-1f4d3f30cd3b · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.871186Z

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.

Observation 7bd158d6-be8f-40c0-b387-a865ace92c22 · outbound

This paper cites In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings (2018).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings (2018)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.858560Z

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.

Observation e2167170-4ccf-4e95-8905-d6f09a6a2778 · outbound

This paper cites In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Commu- nications Security.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Commu- nications Security

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.846231Z

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.

Observation 792a5a31-5633-4bc8-b425-d8434fa40dab · outbound

This paper cites Biologically inspired protection of deep networks from adversarial attacks.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve Biologically inspired protection of deep networks from adversarial attacks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.650427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 956e9753-599e-46e4-8821-07254f8b9e59 · outbound

This paper cites In: 2016 IEEE Symposium on Security and Privacy (SP).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: 2016 IEEE Symposium on Security and Privacy (SP)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.654807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d16e281-b8ae-412d-bda4-99b524e8bb56 · outbound

This paper cites International Journal of Computer Vision 115(3), 211–252 (2015).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve International Journal of Computer Vision 115(3), 211–252 (2015)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.659133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4e6fba-27c4-4edc-ba40-752ecfdfb7f4 · outbound

This paper cites International Journal of Pattern Recognition and Artiﬁcial Intelligence 23(04), 687–719 (2009).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve International Journal of Pattern Recognition and Artiﬁcial Intelligence 23(04), 687–719 (2009)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.819355Z

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.

Observation d4740ada-00a2-4832-b53e-704388128d12 · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.666949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c56f59a-240a-4927-9f21-e6152741e5e7 · outbound

This paper cites Intriguing properties of neural networks.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve Intriguing properties of neural networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T15:15:45.671140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 055823aa-03c0-43d7-9699-a59096192558 · outbound

This paper cites In: Proceedings of the Twenty-Seventh Inter- national Joint Conference on Artiﬁcial Intelligence, IJCAI-18.

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: Proceedings of the Twenty-Seventh Inter- national Joint Conference on Artiﬁcial Intelligence, IJCAI-18

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.799570Z

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.

Observation dbb53023-170f-432d-bfbc-2b2a68fa68cd · outbound

This paper cites In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21 (2018).

Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21 (2018)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:15:45.785531Z

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.

Pith citing papers

No inbound Pith citation observations are available.