Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack | Paper Citation Record

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 2026-08-10T06:31:04.303077+00:00.

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

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

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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

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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 2026-08-10T06:31:04.303077+00:00.

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

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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

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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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

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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

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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

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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 2026-08-10T06:31:04.303077+00:00.

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

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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 2026-08-10T06:31:04.303077+00:00.

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.

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

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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

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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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.

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.

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

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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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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

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Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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.

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 2026-08-10T06:31:04.303077+00:00.

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.

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 2026-08-10T06:31:04.303077+00:00.

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.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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

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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

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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

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

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

Reference 60

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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

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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

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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

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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

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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

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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

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

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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

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

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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

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

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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

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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

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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

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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

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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

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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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.

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Outbound references

Pith citing papers