Paper Citation Record · LEDGER

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

As of 14 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2608.00566.

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

pith.paper-citation-record.v1
2608.00566 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:44:50.294547Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy18
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82e79446-add6-4a3a-b9c6-62a07bdf8115 · outbound

This paper cites ACM Computing Surveys , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors ACM Computing Surveys , volume=

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:54.115740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5fedec81-4b1c-4453-a5e8-cea86b855be2 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.917213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 96eac8b0-a341-400b-88d8-c8f953bcb401 · outbound

This paper cites 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) , pages=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.722085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 085f5ca4-7c6e-4ed7-875b-57f4d5dfd99d · outbound

This paper cites Fooling SHAP with Output Shuffling Attacks.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Fooling SHAP with Output Shuffling Attacks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:48.122138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fc10bb3-ed0c-4f98-9734-9282b71a3dbb · outbound

This paper cites arXiv preprint arXiv:2510.03623 , year=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors arXiv preprint arXiv:2510.03623 , year=

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-05T00:44:50.687846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ad328853-1003-449c-b97a-7d5c609056a1 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:48.273120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c86f4c37-adf1-472d-bb28-31d819d1abb3 · outbound

This paper cites International workshop on extending explainable AI beyond deep models and classifiers , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors International workshop on extending explainable AI beyond deep models and classifiers , pages=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.570714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6eab697-cc0b-45e7-b179-6f6d7f1c18cd · outbound

This paper cites CCF international conference on natural language processing and Chinese computing , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors CCF international conference on natural language processing and Chinese computing , pages=

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.408904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a223e966-eac3-412a-8d5d-3881069f4960 · outbound

This paper cites ACM computing surveys , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors ACM computing surveys , volume=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.250863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0993459e-1629-49e6-9e68-128f9d684be0 · outbound

This paper cites Black Box.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Black Box

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:53.034544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation abce3493-a0ae-47a7-95bf-b73e965ffa72 · outbound

This paper cites , author=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors , author=

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:52.874922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ed88bdce-7af7-4adf-8ee2-44e3ff509928 · outbound

This paper cites ACM Transactions on Computing for Healthcare , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors ACM Transactions on Computing for Healthcare , volume=

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:52.717382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 16d9ab1e-6d10-403b-9358-7d5af05d9927 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:52.556722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 595e274f-1350-4073-a27c-79147a6a1d97 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Advances in Neural Information Processing Systems (NeurIPS) , volume=

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:52.318933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 69fb8295-b777-4f21-b5cf-6344fbb25f77 · outbound

This paper cites Proceedings of the Network and Distributed System Security Symposium (NDSS) , year=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Proceedings of the Network and Distributed System Security Symposium (NDSS) , year=

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:52.121751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d426028a-05a2-472b-a619-066faa9d3bc1 · outbound

This paper cites International Conference on Machine Learning (ICML) , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors International Conference on Machine Learning (ICML) , pages=

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:51.991330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a9a876e6-05fb-4eb7-8052-dcb5b2c549fa · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , volume=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Advances in Neural Information Processing Systems (NeurIPS) , volume=

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:51.802219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7fa489f9-9e16-4f8d-b40d-9882b4b03706 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:49.158563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99bb3e3c-7071-4e9b-aff8-bb5d5da1e200 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pages=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:51.537806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d083ff8-2cb9-4cc7-a7eb-e90242ae626a · outbound

This paper cites 2009 , institution=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors 2009 , institution=

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:49.427058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b163e776-ebea-4d30-ae71-24a875ffade0 · outbound

This paper cites Why should I trust you?.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Why should I trust you?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:51.377426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 899a3f5b-bf6f-4fee-97b4-8c23dc620d97 · outbound

This paper cites 2019 IEEE Symposium on Security and Privacy (SP) , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors 2019 IEEE Symposium on Security and Privacy (SP) , pages=

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:49.691578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5df1647f-2f89-4904-a6b8-66df21574ad8 · outbound

This paper cites Advances in Neural Information Processing Systems 32 , pages=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Advances in Neural Information Processing Systems 32 , pages=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:51.135751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d7a3759-df69-4c02-b1cb-eeb58f423233 · outbound

This paper cites Captum: A unified and generic model interpretability library for PyTorch.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Captum: A unified and generic model interpretability library for PyTorch

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:49.966363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5dd6a8f-6ac3-4a9e-bce7-bfea60bb81cd · outbound

This paper cites 2019 , publisher=.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors 2019 , publisher=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:44:50.898321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06c44dad-bedd-4d72-a710-ed037dbb5bf8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Adam: A Method for Stochastic Optimization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:50.177748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fca6e264-c533-4bf9-a403-863dc1902e15 · outbound

This paper cites an unresolved cited work.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:50.294547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.