Paper Citation Record · LEDGER

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters

As of 17 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2501.14122.

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

pith.paper-citation-record.v1
2501.14122 v2

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:24:45.783395Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

11 of 11 outbound references displayed

verified exact0
verified fuzzy3
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27b0f057-f158-48f1-a935-a2c2d7f9238d · outbound

This paper cites In 2016 IEEE European symposium on security and privacy (EuroS&P), 372–387.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters In 2016 IEEE European symposium on security and privacy (EuroS&P), 372–387

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:24:46.064461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6ee7e7e-bab8-4524-9ab3-2eceb02170ba · outbound

This paper cites Analyzing noise in autoencoders and deep networks.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Analyzing noise in autoencoders and deep networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.762612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c28fbb7-7bf7-4e5b-a39b-9c8a9e895667 · outbound

This paper cites Intriguing properties of neural networks.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Intriguing properties of neural networks

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.783395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe688d67-f100-4e24-8e34-7dff95158e4b · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Explaining and Harnessing Adversarial Examples

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.725191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5c90d16-1753-49fb-94a8-9021bf6cdb08 · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.742703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e536986-3dd6-4595-8215-09e1e5fc3f33 · outbound

This paper cites Adversarial Machine Learning at Scale.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Adversarial Machine Learning at Scale

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.732403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19947c60-394a-451d-bc13-cacdeaffb7e0 · outbound

This paper cites In 2017 Chinese automa- tion congress (CAC), 4165–4170.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters In 2017 Chinese automa- tion congress (CAC), 4165–4170

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:24:46.043166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7338a782-9a34-4868-b513-495799d35a19 · outbound

This paper cites Adversarial Attacks and Defences: A Survey.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Adversarial Attacks and Defences: A Survey

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.711818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d1a3c5f-d70c-41aa-be6a-3342d938a209 · outbound

This paper cites Query-efficient Meta Attack to Deep Neural Networks.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Query-efficient Meta Attack to Deep Neural Networks

Reference 2019

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T15:24:45.977753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 381c5a81-719b-4676-bce6-80a8a252c385 · outbound

This paper cites In Proceedings of the Web Conference 2020, 673–683.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters In Proceedings of the Web Conference 2020, 673–683

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:24:46.026886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be497ad9-7c27-4e26-bd56-ad21472ce62a · outbound

This paper cites Pixle: a fast and effective black-box attack based on rearranging pixels.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters Pixle: a fast and effective black-box attack based on rearranging pixels

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T15:24:45.754980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.