Paper Citation Record · LEDGER

Amplifying Machine Learning Attacks Through Strategic Compositions

As of 15 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2506.18870.

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

pith.paper-citation-record.v1
2506.18870 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:45:44.681189Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:10:51.471822Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T02:07:33.514395Z

Reference resolution

65 of 65 outbound references displayed

verified exact1
verified fuzzy53
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b6449f1-8b08-466b-83a8-5ef0e4357a21 · outbound

This paper cites Srivastava, and Kai-Wei Chang.

Amplifying Machine Learning Attacks Through Strategic Compositions Srivastava, and Kai-Wei Chang

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.634222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9705d72-0d4e-4a4e-8d65-ffa3afed267c · outbound

This paper cites Square Attack: A Query-Efficient Black-Box Adversarial Attack via Ran- dom Search.

Amplifying Machine Learning Attacks Through Strategic Compositions Square Attack: A Query-Efficient Black-Box Adversarial Attack via Ran- dom Search

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.620696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e718edcc-6a8b-45ed-9891-a0b483a491ab · outbound

This paper cites PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable.

Amplifying Machine Learning Attacks Through Strategic Compositions PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.606346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 995ea52d-4cbc-484f-ab94-6296cb789856 · outbound

This paper cites Synthetic and Natural Noise Both Break Neural Machine Translation.

Amplifying Machine Learning Attacks Through Strategic Compositions Synthetic and Natural Noise Both Break Neural Machine Translation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.591823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 425decb7-9f24-4f1e-b5dd-2442cf901475 · outbound

This paper cites Freund, B.

Amplifying Machine Learning Attacks Through Strategic Compositions Freund, B

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.577458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87b66152-6d79-4012-b95d-55d32dcfb350 · outbound

This paper cites Membership Inference Attacks From First Principles.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership Inference Attacks From First Principles

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.422593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a514fa9f-61c7-47bd-ae59-e290dc2e57af · outbound

This paper cites Towards Evaluat- ing the Robustness of Neural Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Towards Evaluat- ing the Robustness of Neural Networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.554671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b83b4011-60a7-495f-b56f-742bffeec01b · outbound

This paper cites Exclusionary Populism and Islamophobia: A comparative analysis of Italy and Spain.Religions,.

Amplifying Machine Learning Attacks Through Strategic Compositions Exclusionary Populism and Islamophobia: A comparative analysis of Italy and Spain.Religions,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.540779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d5286bc9-3e92-41f6-9bd8-d3a85adbb7aa · outbound

This paper cites GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models.

Amplifying Machine Learning Attacks Through Strategic Compositions GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.526113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa28d12f-2490-4b7a-9963-a94d95d898e8 · outbound

This paper cites Jordan, and Martin J.

Amplifying Machine Learning Attacks Through Strategic Compositions Jordan, and Martin J

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.511821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c914f1e2-075c-4cf7-ad04-cbd6eafe1e2e · outbound

This paper cites When Ma- chine Unlearning Jeopardizes Privacy.

Amplifying Machine Learning Attacks Through Strategic Compositions When Ma- chine Unlearning Jeopardizes Privacy

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.496703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95d5c54d-5c47-4144-99e9-2f285f63c512 · outbound

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

Amplifying Machine Learning Attacks Through Strategic Compositions Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.449212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e53deeb5-32b0-493c-99e4-d2ff508355f8 · outbound

This paper cites Amplifying Membership Exposure via Data Poisoning.

Amplifying Machine Learning Attacks Through Strategic Compositions Amplifying Membership Exposure via Data Poisoning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.482298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 923d65bd-6ba5-494a-a4b1-32fad29ccdc5 · outbound

This paper cites BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding.

Amplifying Machine Learning Attacks Through Strategic Compositions BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.468995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 141d77ca-f6e4-4115-bd12-51f83b0105c5 · outbound

This paper cites Now Publishers Inc., 2014.

Amplifying Machine Learning Attacks Through Strategic Compositions Now Publishers Inc., 2014

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.454857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9e082dc9-4834-462b-a2ad-82fba7c9771c · outbound

This paper cites Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration.

Amplifying Machine Learning Attacks Through Strategic Compositions Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.466422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ec5cb4e-0b43-4603-ac94-21e4bad63c55 · outbound

This paper cites Gunter, and Nikita Borisov.

Amplifying Machine Learning Attacks Through Strategic Compositions Gunter, and Nikita Borisov

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.441743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 73f03b2e-76f1-461b-85e0-f3621f0a5004 · outbound

This paper cites Explaining and Harnessing Adversarial Ex- amples.

Amplifying Machine Learning Attacks Through Strategic Compositions Explaining and Harnessing Adversarial Ex- amples

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.427910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a774c37c-0429-4507-82e2-03554ca3b867 · outbound

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

Amplifying Machine Learning Attacks Through Strategic Compositions BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0232bf-d360-48a6-8356-97d793268cd2 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Amplifying Machine Learning Attacks Through Strategic Compositions Deep Residual Learning for Image Recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.412851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0e2748c-20bf-411d-8c02-acdca6d44a1e · outbound

This paper cites Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.489213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0924082b-9e8a-4a35-a82d-09a6fe7a6976 · outbound

This paper cites Quantifying and Mitigat- ing Privacy Risks of Contrastive Learning.

Amplifying Machine Learning Attacks Through Strategic Compositions Quantifying and Mitigat- ing Privacy Risks of Contrastive Learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.398676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 153b2c54-76de-4050-ad3d-b3b269ae1ece · outbound

This paper cites Weinberger.

Amplifying Machine Learning Attacks Through Strategic Compositions Weinberger

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.384862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3211609-477f-4999-a729-41f59dd1959f · outbound

This paper cites Adversarial Example Generation with Syntactically Controlled Paraphrase Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.371013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 729559f3-9705-468d-99b2-50ba50b36b96 · outbound

This paper cites Evaluating Differentially Private Machine Learning in Practice.

Amplifying Machine Learning Attacks Through Strategic Compositions Evaluating Differentially Private Machine Learning in Practice

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.357186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 111d6d3e-ddb1-46b0-a2eb-f497eb2d4561 · outbound

This paper cites MemGuard: De- fending against Black-Box Membership Inference At- tacks via Adversarial Examples.

Amplifying Machine Learning Attacks Through Strategic Compositions MemGuard: De- fending against Black-Box Membership Inference At- tacks via Adversarial Examples

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.342998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2546001-96ba-40b7-9b9f-03ff439b795c · outbound

This paper cites Seitz, Daniel Miller, and Evan Brossard.

Amplifying Machine Learning Attacks Through Strategic Compositions Seitz, Daniel Miller, and Evan Brossard

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.328789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 19ad8e23-52cd-4204-a965-23a89fe0266b · outbound

This paper cites Kingma and Jimmy Ba.

Amplifying Machine Learning Attacks Through Strategic Compositions Kingma and Jimmy Ba

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.314734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 19936014-15a1-4006-bb9c-17dba34eb50a · outbound

This paper cites Exarchos, Konstanti- nos P.

Amplifying Machine Learning Attacks Through Strategic Compositions Exarchos, Konstanti- nos P

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.299386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4450d696-6a4b-443d-893b-ae0d1b4b5af6 · outbound

This paper cites Stolen Memo- ries: Leveraging Model Memorization for Calibrated White-Box Membership Inference.

Amplifying Machine Learning Attacks Through Strategic Compositions Stolen Memo- ries: Leveraging Model Memorization for Calibrated White-Box Membership Inference

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.284827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a447d50f-cbd3-4344-8971-a040cfd6fca3 · outbound

This paper cites QEBA: Query-Efficient Boundary-Based Blackbox Attack.

Amplifying Machine Learning Attacks Through Strategic Compositions QEBA: Query-Efficient Boundary-Based Blackbox Attack

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.269235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0b30deed-8400-47c3-87cd-20cf6f636999 · outbound

This paper cites Morgan & Claypool Publishers, 2016.

Amplifying Machine Learning Attacks Through Strategic Compositions Morgan & Claypool Publishers, 2016

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.254008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 535ec106-3102-44ed-a22f-4f12cea0ed54 · outbound

This paper cites Auditing Membership Leakages of Multi-Exit Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Auditing Membership Leakages of Multi-Exit Networks

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:45:44.794263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9df48682-0a01-47aa-89a9-0057ab92648f · outbound

This paper cites Auditing Membership Leak- ages of Multi-Exit Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Auditing Membership Leak- ages of Multi-Exit Networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.239686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4240e74-5f93-4c65-a663-6fb83d03f4ae · outbound

This paper cites Membership Leakage in Label-Only Exposures.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership Leakage in Label-Only Exposures

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.224841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 82e391b1-956e-40ff-9ac9-fcde1607957f · outbound

This paper cites DEEPSEC: A Uniform Platform for Security Analysis of Deep Learn- ing Model.

Amplifying Machine Learning Attacks Through Strategic Compositions DEEPSEC: A Uniform Platform for Security Analysis of Deep Learn- ing Model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.210683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd443869-0b42-481e-a1d1-ba389dd12132 · outbound

This paper cites Trojaning Attack on Neural Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Trojaning Attack on Neural Networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.196336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4ac40b1-f3b9-4b0a-ba13-dc59628bf980 · outbound

This paper cites Membership Inference Attacks by Exploiting Loss Trajectory.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership Inference Attacks by Exploiting Loss Trajectory

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.562171Z

Source-reported events for the cited work

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Observation 4fea5b69-71d3-4756-8382-3abf287af98c · outbound

This paper cites ML-Doctor: Holis- tic Risk Assessment of Inference Attacks Against Ma- chine Learning Models.

Amplifying Machine Learning Attacks Through Strategic Compositions ML-Doctor: Holis- tic Risk Assessment of Inference Attacks Against Ma- chine Learning Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.566716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60957317-7ca4-4117-beec-10aecc2cbe94 · outbound

This paper cites Deep Learning Face Attributes in the Wild.

Amplifying Machine Learning Attacks Through Strategic Compositions Deep Learning Face Attributes in the Wild

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.171586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2a74b41-bdb2-42c6-83e3-e448cccbb75a · outbound

This paper cites To- wards Deep Learning Models Resistant to Adversarial Attacks.

Amplifying Machine Learning Attacks Through Strategic Compositions To- wards Deep Learning Models Resistant to Adversarial Attacks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.157680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83d10d5a-4df7-439b-9cc9-bc8ad0536771 · outbound

This paper cites Property Inference from Poisoning.

Amplifying Machine Learning Attacks Through Strategic Compositions Property Inference from Poisoning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.142987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59a99784-167e-465d-b8a9-1bf5a2311ac5 · outbound

This paper cites Exploiting Unintended Feature Leakage in Collaborative Learning.

Amplifying Machine Learning Attacks Through Strategic Compositions Exploiting Unintended Feature Leakage in Collaborative Learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.128730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc8c072f-693e-4a6f-9061-abab2ffb25c5 · outbound

This paper cites Ma- chine Learning with Membership Privacy using Adver- sarial Regularization.

Amplifying Machine Learning Attacks Through Strategic Compositions Ma- chine Learning with Membership Privacy using Adver- sarial Regularization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.114242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0f6b889a-3b79-4f55-9766-3ff0107dddf3 · outbound

This paper cites Com- prehensive Privacy Analysis of Deep Learning: Pas- sive and Active White-box Inference Attacks against 15 Centralized and Federated Learning.

Amplifying Machine Learning Attacks Through Strategic Compositions Com- prehensive Privacy Analysis of Deep Learning: Pas- sive and Active White-box Inference Attacks against 15 Centralized and Federated Learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.099997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9afbc8c4-dd2b-471f-a818-a3d4d3f20a90 · outbound

This paper cites Adversary Instanti- ation: Lower Bounds for Differentially Private Machine Learning.

Amplifying Machine Learning Attacks Through Strategic Compositions Adversary Instanti- ation: Lower Bounds for Differentially Private Machine Learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.086107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 66ce2715-0247-42ce-b1b6-2ef9d53cb463 · outbound

This paper cites TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors.

Amplifying Machine Learning Attacks Through Strategic Compositions TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.601353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe2b2fef-2215-4f59-af03-207b0723768a · outbound

This paper cites Technical Report on the CleverHans v2.1.0 Adversarial Examples Library.

Amplifying Machine Learning Attacks Through Strategic Compositions Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.605719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f0d8ce-f5d1-4396-832b-22efe904d0c1 · outbound

This paper cites McDaniel, Somesh Jha, Matt Fredrikson, Z.

Amplifying Machine Learning Attacks Through Strategic Compositions McDaniel, Somesh Jha, Matt Fredrikson, Z

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.071433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 12ed3e02-ff3b-4866-afa7-0f97e35698be · outbound

This paper cites Semantically Equivalent Adversarial Rules for Debugging NLP models.

Amplifying Machine Learning Attacks Through Strategic Compositions Semantically Equivalent Adversarial Rules for Debugging NLP models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.056906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75533ef2-c9c5-4815-9255-e89aad238cb7 · outbound

This paper cites White-box vs Black-box: Bayes Optimal Strategies for Membership Inference.

Amplifying Machine Learning Attacks Through Strategic Compositions White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.043090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c270c1f6-0563-4ef8-83f3-d40b3653f7e7 · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Amplifying Machine Learning Attacks Through Strategic Compositions ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.028719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87825be1-987b-485a-85f0-f0913c38e55b · outbound

This paper cites Membership Inference Attacks Against Machine Learning Models.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership Inference Attacks Against Machine Learning Models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:45.014351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1888a72c-8dff-437f-bb2a-fee02756d933 · outbound

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

Amplifying Machine Learning Attacks Through Strategic Compositions Very Deep Convolutional Networks for Large-Scale Image Recog- nition

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.999413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb08c8cc-f71b-45eb-b6b0-8101014a7d04 · outbound

This paper cites Overlearning Reveals Sensitive Attributes.

Amplifying Machine Learning Attacks Through Strategic Compositions Overlearning Reveals Sensitive Attributes

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.985257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83ba3dcd-f558-4370-9e4a-7a602049e847 · outbound

This paper cites Stanfill, Margaret Williams, Susan H.

Amplifying Machine Learning Attacks Through Strategic Compositions Stanfill, Margaret Williams, Susan H

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.970237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ddf473c2-f533-460c-8967-35719f5dd6d2 · outbound

This paper cites Intriguing Properties of Neural Networks.

Amplifying Machine Learning Attacks Through Strategic Compositions Intriguing Properties of Neural Networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.956329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 18759f41-9b02-4357-811d-810b0f986e05 · outbound

This paper cites Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models.

Amplifying Machine Learning Attacks Through Strategic Compositions Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.650820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d110ad2e-8ad7-4609-94ea-7dcd0c3439f9 · outbound

This paper cites Gunter, and Bo Li.

Amplifying Machine Learning Attacks Through Strategic Compositions Gunter, and Bo Li

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.941619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e5aad50-5ed8-48a7-86c3-763cb1ac64f7 · outbound

This paper cites Se- curityNet: Assessing Machine Learning Vulnerabilities on Public Models.

Amplifying Machine Learning Attacks Through Strategic Compositions Se- curityNet: Assessing Machine Learning Vulnerabilities on Public Models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.926367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 50801164-5108-47e0-baa5-6a5e71903723 · outbound

This paper cites Membership Inference Attacks Against Recommender Systems.

Amplifying Machine Learning Attacks Through Strategic Compositions Membership Inference Attacks Against Recommender Systems

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.910989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 382b5cad-6be1-464f-ace2-f0397f024be4 · outbound

This paper cites ViT-YOLO: Transformer- Based YOLO for Object Detection.

Amplifying Machine Learning Attacks Through Strategic Compositions ViT-YOLO: Transformer- Based YOLO for Object Detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.895617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9903b9be-635e-40f3-a26f-e086164fff6d · outbound

This paper cites Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments.

Amplifying Machine Learning Attacks Through Strategic Compositions Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T18:45:44.672442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09043879-cfd5-4d8a-a549-56853915fc1d · outbound

This paper cites Places: A 10 Million Image Database for Scene Recognition.IEEE Trans- actions on Pattern Analysis and Machine Intelligence,.

Amplifying Machine Learning Attacks Through Strategic Compositions Places: A 10 Million Image Database for Scene Recognition.IEEE Trans- actions on Pattern Analysis and Machine Intelligence,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.877905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c9755593-b411-4189-9837-3181bb223723 · outbound

This paper cites Property Inference Attacks Against GANs.

Amplifying Machine Learning Attacks Through Strategic Compositions Property Inference Attacks Against GANs

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:45:44.862915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 368abc4b-60de-4ff8-9534-f9692ef4c05b · inbound

SoK: Colluding Adversaries in Machine Learning Pipelines cites this paper.

SoK: Colluding Adversaries in Machine Learning Pipelines Amplifying Machine Learning Attacks Through Strategic Compositions

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.515930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.