Paper Citation Record · LEDGER

Adversarial Defense by Suppressing High-frequency Components

As of 16 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 1 inbound Pith citation observation for arXiv:1908.06566.

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

pith.paper-citation-record.v1
1908.06566 v3

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:44:43.081462Z

measured 11 of 11 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T01:38:39.015952Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:56.070149Z

Reference resolution

10 of 10 outbound references displayed

verified exact0
verified fuzzy9
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b5f7ebba-ade3-4759-a58f-62997f19ea48 · outbound

This paper cites Ijcai-2019 alibaba adversarial ai challenge.

Adversarial Defense by Suppressing High-frequency Components Ijcai-2019 alibaba adversarial ai challenge

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.215449Z

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 6ddfbfeb-ea7b-4e69-b16d-a8803e9a7bee · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Adversarial Defense by Suppressing High-frequency Components Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.204017Z

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 88244ad0-87c0-4fa6-83cc-c6d82bca8ef0 · outbound

This paper cites Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression.

Adversarial Defense by Suppressing High-frequency Components Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.193044Z

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 922be6b4-10b6-4c2d-9c6e-671949ba1221 · outbound

This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

Adversarial Defense by Suppressing High-frequency Components Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.181065Z

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 51c1a07f-0798-48ae-a94e-d00b29052e59 · outbound

This paper cites Explaining and harnessing adversarial examples.

Adversarial Defense by Suppressing High-frequency Components Explaining and harnessing adversarial examples

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.168968Z

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 dc8e1bea-0948-4b67-9b60-87308b4070e3 · outbound

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

Adversarial Defense by Suppressing High-frequency Components Towards deep learning models resistant to adversarial attacks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.157606Z

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 678da8d3-f011-4f09-b596-2321249070ca · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Defense by Suppressing High-frequency Components Intriguing properties of neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.145972Z

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 2a689d60-04b8-4d18-84ec-769d0e5623b0 · outbound

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

Adversarial Defense by Suppressing High-frequency Components Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.133592Z

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 72fc61f8-074d-420e-9d7f-80adb43bc12c · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy.

Adversarial Defense by Suppressing High-frequency Components Theoretically principled trade-off between robustness and accuracy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:44:43.122037Z

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 eb57933b-546e-4dad-a152-2431d6d31a53 · outbound

This paper cites write newline.

Adversarial Defense by Suppressing High-frequency Components write newline

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T12:44:43.081462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 155f69ec-7242-4896-89f2-3454bed1403d · inbound

TaFD: Threat-Aware Frequency Decoupling for Adversarial Robustness against Heterogeneous Attacks cites this paper.

TaFD: Threat-Aware Frequency Decoupling for Adversarial Robustness against Heterogeneous Attacks Adversarial Defense by Suppressing High-frequency Components

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:56.071986Z

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.