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Paper Citation Record · LEDGER

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness

As of 15 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2412.19947.

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

pith.paper-citation-record.v1
2412.19947 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:50:58.710431Z

measured 14 of 14 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

14 of 14 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2401e3f5-7a42-4c9b-a356-3db36f4e0da0 · outbound

This paper cites Improving Adversarial Training using Vulnerability-Aware Perturbation Budget.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Improving Adversarial Training using Vulnerability-Aware Perturbation Budget

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:50:59.159375Z

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.

source=pdf_text observed=2026-08-10T23:50:58.603780Z digest=sha256:82374d1ed828d8ce24f1b5b10ae535f22a127bcb8713b4c14fe2403e6da92457

Observation 64230407-7680-44c5-b65a-a7ff586aa873 · outbound

This paper cites CAT:Collaborative Adversarial Training.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness CAT:Collaborative Adversarial Training

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:50:59.040770Z

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.

source=pdf_text observed=2026-08-10T23:50:58.638782Z digest=sha256:de8421aa7ac437115504ba97abdfd8f23e73bb374f37e54f8df6d8f3e4a0f845

Observation ba0faf63-4250-4758-80d3-c7963d5384cc · outbound

This paper cites Practical black-box attacks against machine learning.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Practical black-box attacks against machine learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.660988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.660988Z digest=sha256:4f703a63196d1ef316d92b3c70e0e324e1b361d5574a61543e2f71141413c639

Observation 7b4eeaac-b965-4b04-a4f4-c413ce0bd7e4 · outbound

This paper cites Improved Adversarial Robustness via Logit Regularization Methods.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Improved Adversarial Robustness via Logit Regularization Methods

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:50:58.892501Z

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.

source=pdf_text observed=2026-08-10T23:50:58.688651Z digest=sha256:fb68195fed333a9999eaacc51a56552241918edf2ac05141bc88b42cee6157cf

Observation d57014b8-d52b-4b60-9699-b826ad60dd3f · outbound

This paper cites Intriguing properties of neural networks.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Intriguing properties of neural networks

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.701067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.701067Z digest=sha256:10f8f505ae9ddd92baa61b74618649833da21f0643e91c05012c6d9c8d49f6c1

Observation 7ffe4f2c-1c9f-46c1-9919-b3c417398018 · outbound

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

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Fast is better than free: Revisiting adversarial training

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.710431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.710431Z digest=sha256:e1bb3c8d2c0ea6aab5ad71b004f93e57520e89db7a832dce44888bef6698751c

Observation 802f95ae-7788-49be-945c-d15a1b91f9a4 · outbound

This paper cites Stochastic Activation Pruning for Robust Adversarial Defense.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Stochastic Activation Pruning for Robust Adversarial Defense

Reference 2009

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.566530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.566530Z digest=sha256:896641e0cb0ffc8cb83ce3720fcf18192abb263a6c27acabfeb3d10c17e6290a

Observation 2af36198-c2b9-463d-9224-aba22b01391f · outbound

This paper cites Logit Pairing Methods Can Fool Gradient-Based Attacks.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Logit Pairing Methods Can Fool Gradient-Based Attacks

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.651453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.651453Z digest=sha256:5bb7fe8f9667f8d95cbaa5f8c69638a9aa4720f29f864212f49248d597f60038

Observation dbbc8461-bcbd-4373-b79b-2130e2af7de1 · outbound

This paper cites Extreme Miscalibration and the Illusion of Adversarial Robustness.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Extreme Miscalibration and the Illusion of Adversarial Robustness

Reference 2017

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:50:58.964272Z

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.

source=pdf_text observed=2026-08-10T23:50:58.667658Z digest=sha256:2eb8ee13c1337ebdc74b0f344c23a79fd30d3f990750da16c82efbc1ca83a845

Observation d8d4c4b1-d0a6-4604-a574-3a778fc230ce · outbound

This paper cites Evaluating and Understanding the Robustness of Adversarial Logit Pairing.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Evaluating and Understanding the Robustness of Adversarial Logit Pairing

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.584178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.584178Z digest=sha256:77939208accbee40cfded1142cbc42e2c33bb5435acc8eae616968d56d36dfe3

Observation 825c10ba-7310-46c4-a0b8-8e3c9831e0bc · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.677970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.677970Z digest=sha256:e7351765a118b348b193beefc5d35ce1a018916b330ef6b7135b95dbf7d1ea39

Observation 12e25236-5647-464b-95ab-543f7227d219 · outbound

This paper cites Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.629331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.629331Z digest=sha256:ee121f8c18bb33de85e4d679624b901f061fbafd06813bc942ddac686a12f7da

Observation 2386052f-c59b-47d0-ba5f-8d90b45ad265 · outbound

This paper cites Adversarial Logit Pairing.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Adversarial Logit Pairing

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.621274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.621274Z digest=sha256:94b0810ba84f77e254c2b2e7fef314e0b167a14329fdc5218662ac4fe6abc8cc

Observation 0d497000-b498-41be-9d41-4a9293b3961d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Explaining and Harnessing Adversarial Examples

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T23:50:58.612716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:50:58.612716Z digest=sha256:333f9470e442687e849b8544c93d22134055d55f83271bbc515c0f59287fcfdf

Pith citing papers

No inbound Pith citation observations are available.