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Paper Citation Record · LEDGER

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack

As of 19 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 1 inbound Pith citation observation for arXiv:2507.05441.

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

pith.paper-citation-record.v1
2507.05441 v1

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:31:06.243141Z

measured 76 of 76 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T06:17:07.660975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:43:31.568829Z

Reference resolution

75 of 75 outbound references displayed

  • verified exact26
  • verified fuzzy5
  • unresolved36
  • parse uncertain0
  • malformed identifier4
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e9096f2b-8838-4c8f-b95c-36c606a0ffb5 · outbound

This paper cites Cornell Research Report On Enron 1998 | PDF | Enron | Discounted Cash Flow.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Cornell Research Report On Enron 1998 | PDF | Enron | Discounted Cash Flow

Reference 1

Resolution
verified exact
raw_fallback, observed 2026-08-06T19:31:07.846295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:30:59.334749Z digest=sha256:29856f3e9cadd8caf5b8fcda872f86dab8464c4b1fc76c2b060e8d6149d51969

Observation e4e40aea-bdbc-432d-8e98-4ced77a8a778 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 2

Resolution
verified exact
raw_fallback, observed 2026-08-06T19:31:07.779233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:30:59.423336Z digest=sha256:34f66425bbd786438f2b75f8bc828537617737ec8b8f4b44d590622302b85cb7

Observation 825a0b58-b81b-4770-9be3-a2666a94ef43 · outbound

This paper cites Real Attackers Don’t Compute Gradients.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Real Attackers Don’t Compute Gradients

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T19:30:59.520411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:30:59.520411Z digest=sha256:bf506f9cc2c32876c0f28de4e37f2ca41b918be84ff05cfe3ff4613f966a291a

Observation 5d14e9ec-d4fe-4db3-991a-e2e48a3672d5 · outbound

This paper cites Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T19:30:59.640346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:30:59.640346Z digest=sha256:0cec5642e1ccc9bb2b6d774263b3c1c0beea78db95064f186138a943df8d2656

Observation f910eea0-df6b-4d35-b315-350b3194e918 · outbound

This paper cites JULIA YU, and JIE ZHANG.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack JULIA YU, and JIE ZHANG

Reference 5

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T19:31:06.566232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:30:59.785959Z digest=sha256:2ca2c9fe0521d54b50b11a08aad55afbf6ea6172408c5a4c4a3ad72c4b22144f

Observation be742298-bc93-47a6-8a5f-6b352fe40ab4 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.101525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:30:59.847805Z digest=sha256:47b4fbf8e361f6384917d26fca0ec4defcf66f12d8bbb38247d37182f0310d93

Observation 085c2488-7941-4280-9c25-bbecf6fa26ae · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.089988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.003341Z digest=sha256:b1231cb679803775d46513529acba9d3444061b59f7bf30ec3de58e3cb516d4b

Observation 850cfabb-02ed-43cc-a944-ece2e4f85406 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 8

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.554581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.073903Z digest=sha256:0dcf3eac2db26956419c3e5598f5f3fdaa3153588975d1f238875981f903becb

Observation 61802f14-427c-4b87-bff9-746929d1d2bb · outbound

This paper cites Beneish, Charles M.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Beneish, Charles M

Reference 9

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.543314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.154219Z digest=sha256:0c30c766dbfc614d89c8e12c05558201db4865ff58ee36a925875c8e46ec40e3

Observation ab841c77-c9b8-4edd-b7a6-39654b75edf2 · outbound

This paper cites Beneish and Craig Nichols.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Beneish and Craig Nichols

Reference 10

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.530266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.242060Z digest=sha256:14127eabe388fbdf794ea2e9d2b8a7b4ca69e144973ab23db6d4a0c493088b3a

Observation 44506709-ebf6-4115-9758-b124e5ec8683 · outbound

This paper cites Beneish and Craig Nichols.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Beneish and Craig Nichols

Reference 11

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.519412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.284457Z digest=sha256:bd2d93d95f91b33de7edd3c1b54bc44ff7217fa3355d48295e9bab4038b5f6e1

Observation f62b44f9-b74f-4d19-93ff-5e5e6d8c4cb0 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 12

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.508019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.351533Z digest=sha256:bee0ff08e29ea8550dbd23b9f222448c1835add519a95ffc02d03e78ece4ab3f

Observation 3d4aa4fa-2aa0-44c7-90db-78acb3f41b08 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:00.426752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:00.426752Z digest=sha256:47a9d5220eb6931fba6e892390187a63661d4e3bdeedbb2702da6dad30bd7cef

Observation e4313f00-7bc3-4533-81e3-d54ad1036cec · outbound

This paper cites Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:00.518171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:00.518171Z digest=sha256:e5993f66a7188c0e84e2fe5f489db1b6949731a6b44b98c8113344be9bc4d1c4

Observation e8d61b8d-e3f7-4f99-b0df-0a16fda74e13 · outbound

This paper cites Efficient and Modular Implicit Differentiation.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Efficient and Modular Implicit Differentiation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:00.595705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:00.595705Z digest=sha256:9b6e4fbb75da61093cb6b6ca81217da270ccd6fb9efa26ed0b6b8d5a39fd0723

Observation be697586-481d-42bb-91a7-beb6b9f23a72 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.078602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.709222Z digest=sha256:b79df551404fb371f9a2f26d4efe1bb5f95fdad62dfd5827ef602f7d58c6fb7f

Observation e9561ec4-4516-4264-ad64-9aac44697868 · outbound

This paper cites Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:00.800605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:00.800605Z digest=sha256:0ab89ace4a70a1afcb6571e71e3a4172ba877eb9666e64f72a99a00d2725dca0

Observation a22daa2c-7488-48e6-abc1-1e760b09b9d2 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.065769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:00.879829Z digest=sha256:1c97870b86692361ddf7487dfbc1a71a694e66cc561cec7f724ff977954f049b

Observation e941e346-f608-4269-aa6c-193a4c956cbc · outbound

This paper cites Extracting Training Data from Large Language Models.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Extracting Training Data from Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:00.983161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:00.983161Z digest=sha256:6ecf603361a19cc29a3462ceb6119db994a289b074fa9e3b5de07a0324e21db7

Observation 9d09bd1a-9fe9-4d40-9df8-1757428f7079 · outbound

This paper cites The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:01.119521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:01.119521Z digest=sha256:c902fc8a4db3d01ea6481cbba3f8112cd40833489cd8ab06045f862fa9f2a3f1

Observation f99b7233-089a-4a4d-b0fc-a56d863457d8 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:01.216373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:01.216373Z digest=sha256:502d3967c6784fd671ec8f6d1d9b49d2e948030fe496c9aae8b819a806a8c723

Observation 53f3a780-0b14-417b-b88a-af525ea93256 · outbound

This paper cites $\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack $\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:06.471850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.290307Z digest=sha256:f605d996104e1fdd796c9800cf2fe000faa2dc0b36de61c1437ca6c5fc33f70a

Observation dc0f6c11-7bf5-4c72-a118-20f132d7d14a · outbound

This paper cites Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:07.407612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.310794Z digest=sha256:02acfdd181d292aacccc175bd8ba85e353ffb1a2dc36c83c8d6a80528ec57650

Observation cdf34f98-3832-456f-bcc7-cf6191f419f4 · outbound

This paper cites Dechow and Ilia D.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Dechow and Ilia D

Reference 24

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.452250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.400845Z digest=sha256:149e6356d9131c08517f48ea5a399df05bb6abc8b3af74e75a32613d5a26565b

Observation 4fa73389-8a55-46d8-a19c-9d3f417ee210 · outbound

This paper cites Dechow, Richard G.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Dechow, Richard G

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:31:08.053245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.511390Z digest=sha256:6214875c3d492c0d8017a8493a6dfa63fef9fbc836be6a2568f7bb505e428b2c

Observation 6d99efda-0f0a-4027-b72b-a418cff154b7 · outbound

This paper cites DeFond and James Jiambalvo.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack DeFond and James Jiambalvo

Reference 26

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.441025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.762122Z digest=sha256:820d3b128d913806a0939de3fa32c04dd93f52eaac59701de724c63293bec695

Observation 395b6d73-11a9-41a1-84dd-197f6d877f2f · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.040592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:01.911615Z digest=sha256:08e06e9b556b25dafdc2ca1debc8877dcc4461c8ad7ae85a461bfdfdefb6b22e

Observation 15fad7d4-6b00-4a9b-bd01-0c829216708a · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:08.016372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:02.272970Z digest=sha256:15fc48afa0241880c59038b565e7f732672a750eac8330b712020cd040ac0b5f

Observation 94ffdbf6-e959-4a7c-b9cf-c2ceeece5a3e · outbound

This paper cites Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:07.389779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:02.453175Z digest=sha256:d63e448f5fac84ca8d54ba10fe1f8b96c4c264a9d2c997f1699f2eb2a21a19b9

Observation 76ea5417-9bad-48cd-ac94-c177b19e15db · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:02.616791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:02.616791Z digest=sha256:ddbdb62f37fa0e7b63aafaa39d25de1077a94de5abd9020318469c3f3934c9a5

Observation 3059d2e9-f2a4-44e2-bc67-03557bc90fe8 · outbound

This paper cites Carlin, Hal S.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Carlin, Hal S

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:31:08.004014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:02.767846Z digest=sha256:53eff54157735951b7cf6cff1a55a6e496dfd1ba8c5539028bf3450a540a146b

Observation a271ae19-8494-4944-944a-2af9741f14d4 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.992229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:02.962580Z digest=sha256:f0fca1fa2a60e1d74e88e4882c6f62b25bb5d8a9b38f24e621af2cf0eab6b29a

Observation 935f5de7-09b3-456c-8204-70e8bab24b80 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 33

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.421733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:03.093222Z digest=sha256:2743dbaec6ac20cea2aec05cce6bd0c2489e0a79af8a8f1496814773daf778a0

Observation 8f8e4aeb-d6f0-4bd8-8a0a-e1889e19d812 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 34

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.410260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:03.347831Z digest=sha256:840338685f95c17527b8fa389f4e79b937840f52454e4bf5bbf503df451457fb

Observation bdd6490d-414f-4444-94c1-9847c0ef558f · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 35

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.398275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:03.524358Z digest=sha256:1133249de22f3625e8da9b9511af1d50e31c510718abdada0b0d0f031f1edb9f

Observation 5fe652c0-b971-4940-80be-9ffa5f98b779 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 36

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T19:31:07.979465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:03.670899Z digest=sha256:6543d18041ab52c8404c914dcc26e2af6a31e4ef4b9e0cf7cff0cf71ef3a3187

Observation 5f570b33-80f2-4bed-8951-805aa35a43b9 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:03.842345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:03.842345Z digest=sha256:f4712ae5c59ba50f6eb7b7d25fab45d318470ee516d075d5c86cc2fd9babf91c

Observation 64da8f70-95e7-4784-849c-e2e7058d41ad · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:04.027142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:04.027142Z digest=sha256:bd5b4c84c79e0f531f2d7efc7ffa54ad26398d97351ab834d2ce7911b2046cf2

Observation 00dea18c-b95f-4c2d-bf27-73b36b400f7a · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:04.194819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:04.194819Z digest=sha256:e72a5dda860d0c8edd7c370a33412f3f01eaa34179d103763d6980237f9b0a16

Observation e8ff27a6-2f44-4ce6-b7eb-358dfadcedbc · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.966537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.316288Z digest=sha256:081f78f8cd1018b09983b76d25b673a509c476751c883cf3d63c72d35d9afa98

Observation 98ff2931-c464-45e8-9e42-414870fd4b3a · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 41

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T19:31:07.141067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.476569Z digest=sha256:700e299c328c0da6db2466195364bb80be762ae9cf55cac2e9fae0aa76c7bd26

Observation 5d864e0f-29a3-435e-998b-d221dabf6c3c · outbound

This paper cites Inverse spectral problem for a third-order differential operator with non-local potential.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Inverse spectral problem for a third-order differential operator with non-local potential

Reference 42

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T19:31:07.064909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.514402Z digest=sha256:d70e995aca0363763a46cd25e82d549850d23d93ae3aef624623d150a7c90cf5

Observation c078ce24-f77b-419d-a0a1-ba37a8bc6d09 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:04.637779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:04.637779Z digest=sha256:0dbcfd0b7b1e8b66e3d5252827819e6f2c93fa784a06a2719dda4d1145497560

Observation 26bccd26-5735-4c8f-8338-8693aa95f86c · outbound

This paper cites Continuously Generalized Ordinal Regression for Linear and Deep Models.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Continuously Generalized Ordinal Regression for Linear and Deep Models

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:07.046135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.745977Z digest=sha256:f369d27a3574916794f7cfc9debcbec9dcc9af84f2a2e3afef3adc67c44885a7

Observation 873b2e41-7e2d-45be-9e8c-699db88eb195 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 45

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T19:31:06.385856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.842518Z digest=sha256:362f4c3385e7d1ee923673b16db60ce1d417cc313e3634144d7fea0f6c6128c6

Observation 47afb885-8bbf-4ff7-b813-5264ba0b7f67 · outbound

This paper cites Investigating Human Priors for Playing Video Games.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Investigating Human Priors for Playing Video Games

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:04.999970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:04.999970Z digest=sha256:7ac7a2325d8c7ed4751c5fcecf2280ae6104be34c2d1a7d68011be5cdc702d35

Observation 9951ae34-0244-4f9a-9bb1-780f509aa6ab · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.955026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:05.077230Z digest=sha256:9638b0a2d2a04a82ba47fdd1981fc8b1dc60c52deef803451d652f7f9a373aaf

Observation 886adfa7-2243-4a1b-ae24-726c57c308d4 · outbound

This paper cites Casimir functions of free nilpotent Lie groups of steps three and four.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Casimir functions of free nilpotent Lie groups of steps three and four

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:07.014473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:05.154024Z digest=sha256:c6756c12b3576e5be5563dea8fe73d71223a0c171c05b632bbbffa559c22523e

Observation a6530f3e-be2d-4997-af69-248375199950 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 49

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.373390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:05.193744Z digest=sha256:439dc65ec4ff9e1997e4ef696c145e07595ae442dab834ef523a2ac88e450ab4

Observation 6b67c388-7958-4256-997f-d93f2f6ab2b1 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:05.274500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:05.274500Z digest=sha256:14bf683f80402034269297f95aca4739e4958ca5d04e09c18bc15e178fa0c1a7

Observation 1fefbb73-598c-4858-b186-dc1b56991533 · outbound

This paper cites Adversarial Attacks, Regression, and Numerical Stability Regularization.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Adversarial Attacks, Regression, and Numerical Stability Regularization

Reference 51

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T19:31:06.361150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:05.447214Z digest=sha256:dd66cf6a4b65930f35849f2090a90bfdd25da4b423c25414a2224167ff339e45

Observation ab4cc8ee-3e6e-4ca6-b680-3dbec7ad224a · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:05.602297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:05.602297Z digest=sha256:0a370458d278b05dbe21741ca58b6a59ac0ab96c06b1fdb4a5ab2c6c1b26fefb

Observation ce199876-f60b-41b9-a65d-b1de6ea272ba · outbound

This paper cites Piotroski.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Piotroski

Reference 53

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.344164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:05.831130Z digest=sha256:b07f363b80a2750c14992c714c09185c150b70adbb637bdf4e204115003736af

Observation d1b08ec4-d1d4-4f30-a6e5-41aee001c48e · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:05.962715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:05.962715Z digest=sha256:bc0539c22a6d241bf940dfcd06201cc28062426ad03722b77a8f6db90b721bb0

Observation 75b8b85d-5eb8-43e2-9d5d-5a24d7335ea7 · outbound

This paper cites You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:06.843698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.088539Z digest=sha256:eeea69b5b70fbe9ce173411eb569244c3dd981216451302ee7856936a85ed630

Observation 56a9de09-995c-4461-bb39-7f89cd771770 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.921688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.148773Z digest=sha256:868a6e5760c3ebbc83bf85a4f2165afcdf2bb1bd3e2129fe8c5195451920eb86

Observation 930e781b-d0db-4221-a0f9-9e4aa67940d3 · outbound

This paper cites Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory

Reference 57

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T19:31:06.826095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.193039Z digest=sha256:87abcccdd1792820c0538694734d0d9c8d206bb39294b73378006ac8d156cd1c

Observation d65a66c7-86cc-4ae7-bed1-18d02120abfb · outbound

This paper cites Martínez-Romero, and Teresa Mariño-Garrido.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Martínez-Romero, and Teresa Mariño-Garrido

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:31:07.909899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.197279Z digest=sha256:61178daee79041efbfb653d2fdf83b420087d53635a4ba47fed524d4063988c3

Observation e64ddd08-5258-4d2c-9682-f4fd8e02ee9d · outbound

This paper cites Ribeiro and Thomas B.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Ribeiro and Thomas B

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:06.205103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:06.205103Z digest=sha256:dc28d8176b2d6fe80d5bc513bb263039dc2a5245d0c9e9c1fb9fb8631f0b1211

Observation 30afbc3c-d266-4c9f-b735-4a854033005d · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 60

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T19:31:06.318953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.209379Z digest=sha256:b0d74b4271691bfe8a614aabe0d9e51c896d1b5a9ac200161b91dea8bb80fba5

Observation 5b364842-742a-4952-8a81-3d3204e56f95 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:06.213095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:06.213095Z digest=sha256:26c42bc8e9bb2482aa1240ee6d57c5bd4d1f8e2cd7fe579759b0724340f8a466

Observation 9d0aad24-f119-4f1c-93f2-55e14e13c116 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 62

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.308606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.216331Z digest=sha256:87c8f2d6b267304e4fcbe2068ee54289699d49751511e03efbcd5336dd3adce4

Observation cb87ab62-4e6e-46bd-96c9-dc487b7f8c22 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.896171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.219792Z digest=sha256:c3848f2576edca6313c2aefb2c2d7e816fad69dd2552aa63de0f3ac0bf891b00

Observation 847ebbb1-37b5-403d-a465-f486a47e8cb2 · outbound

This paper cites European Journal of Family Business 7, 1 (Jan 2017), 41–53.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack European Journal of Family Business 7, 1 (Jan 2017), 41–53

Reference 64

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.332049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.201346Z digest=sha256:4718d2e20bc3f46fb30a4d559736a48c733e2afc8c137d83abed170e5f9cbee4

Observation 42917526-1a21-49a0-9c77-ecf9a7dc3c44 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 65

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.298344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.225798Z digest=sha256:cfca4b64188293c5cb59dc1edf38296b84473bc6426f7bb5a7b59b8cf615396b

Observation 21aefdbc-a188-4089-85ad-e79e2df9424f · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 66

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.287620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.228925Z digest=sha256:bfed9e1c8e080c90674096727bf615c2b8d40de30530b3afb1500dc35c2cebbc

Observation 8aa4b244-ad12-404f-acab-25fb5c57b92c · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 67

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.871890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.232820Z digest=sha256:cd34e3e91782633f619e91c84c130a6415dd8698f08ab0aa64ed189b86ceaeea

Observation f1f07793-af1d-4025-9e0e-2f333588db1f · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 68

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:31:07.858472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.236592Z digest=sha256:7b7d64395c39694213f9e692843367bbbf5f9305f42d78629986605a442ca2a4

Observation 84eed8c6-632a-422c-b82d-6f446fe98bb6 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 69

Resolution
verified exact
doi, observed 2026-08-06T19:31:06.275424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.239881Z digest=sha256:81d393e3ddfcc6edb6c3e73c7f9b564abedae1741975c2ed57caadd98bf8e5c1

Observation 8147b814-2223-4c36-a6fc-11b3cd0b614a · outbound

This paper cites Simko, J.S.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Simko, J.S

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:31:07.884195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:06.222877Z digest=sha256:c76ba05d2769bf29e710978e58c64029c8328c1e4dcc64bc4fd71bbc155a4003

Observation 850f4ce6-60d8-4b11-a58c-dd26cce808b0 · outbound

This paper cites an unresolved cited work.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack Unresolved cited work

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:06.243141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:06.243141Z digest=sha256:bdb8b374960cf09caf990348aeb0a46dc130a6ef2c9a7540ba6eb3a035c57fb9

Observation fb21736d-be0a-4647-9238-e1d3067d34da · outbound

This paper cites InProceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack InProceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:06.002449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:06.002449Z digest=sha256:d474a8a455fe3f0d77846e6d550ba552d9a3de3341800862b94137e9e1bcb8b3

Observation f31576b8-1d28-4d5b-bc6c-f7fbedc64df0 · outbound

This paper cites In 2020 IEEE Symposium on Security and Privacy (SP).

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack In 2020 IEEE Symposium on Security and Privacy (SP)

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:05.726841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:05.726841Z digest=sha256:2be9f8447554d671229bce0c536ec35dea89ca9252d0e4ca600e2af0f0cc4e5e

Observation 7d311886-5fb3-45d3-bce4-16b338617a6f · outbound

This paper cites More Options for Prelabor Rupture of Membranes, A Bayesian Analysis.

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack More Options for Prelabor Rupture of Membranes, A Bayesian Analysis

Reference 2024

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:31:07.158098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:04.430687Z digest=sha256:929dbd512c57f982db70e0f8569ec2a8cca9e95e7c3cde398bf3f50b71930a26

Observation 1ff10704-be11-4a29-bc3f-46d7ac598a5b · outbound

This paper cites In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR).

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:31:08.028993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T19:31:02.035829Z digest=sha256:36cf28da63792f0b22a53917b4bbae59c4c39824536502d148da10b00bcbe2f4

Pith citing papers

Observation 53478191-7b29-4514-b2fa-3dc4472cf9bc · inbound

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech cites this paper.

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:43:31.570242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-29T06:17:07.660975Z digest=sha256:82561885ff5c2207fb89ab36f13253f49eca8c9d43ae999104118ff98bf8a01a