Paper Citation Record · LEDGER

Ensemble Adversarial Training: Attacks and Defenses

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:1705.07204.

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

pith.paper-citation-record.v1
1705.07204 v5

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:41.197374Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

1109
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4e23e29b-9774-4a26-b6b8-1feb8b8234c0 · inbound

Fooling a Real Car with Adversarial Traffic Signs cites this paper.

Fooling a Real Car with Adversarial Traffic Signs Ensemble Adversarial Training: Attacks and Defenses

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.742702Z

Source-reported events for the cited work

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Observation d68def8e-adaa-45c3-a5f1-02ed1b72d057 · inbound

Affine Disentangled GAN for Interpretable and Robust AV Perception cites this paper.

Affine Disentangled GAN for Interpretable and Robust AV Perception Ensemble Adversarial Training: Attacks and Defenses

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-25T01:56:32.519280Z

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Observation 13b161c1-f6ec-4bc6-ac0f-02c8a2f673ec · inbound

Unsolved Problems in ML Safety cites this paper.

Unsolved Problems in ML Safety Ensemble Adversarial Training: Attacks and Defenses

Reference 191

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:45:27.546632Z

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Observation 5061c0fe-45d8-4e4f-b0bb-dc30ffaf0e6d · inbound

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models cites this paper.

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models Ensemble Adversarial Training: Attacks and Defenses

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-05-25T09:05:35.816389Z

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Observation 8a68ac40-1b1b-4bb2-833d-297827a7113c · inbound

Towards Generalized Certified Robustness with Multi-Norm Training cites this paper.

Towards Generalized Certified Robustness with Multi-Norm Training Ensemble Adversarial Training: Attacks and Defenses

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:03:24.494913Z

Source-reported events for the cited work

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Observation 582ae296-6bba-4860-9a73-99cdaca4d3cd · inbound

Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion cites this paper.

Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion Ensemble Adversarial Training: Attacks and Defenses

Reference 25

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Observation a5f60b0f-6f56-4bdc-982a-b85ac909052c · inbound

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition cites this paper.

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition Ensemble Adversarial Training: Attacks and Defenses

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:55:10.270586Z

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Observation ea67c754-cc8b-4734-bb88-873643d07d82 · inbound

Are classical deep neural networks weakly adversarially robust? cites this paper.

Are classical deep neural networks weakly adversarially robust? Ensemble Adversarial Training: Attacks and Defenses

Reference 4

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Observation fb0976df-d0a7-4395-96e8-6f1eb14a985f · inbound

Exploring Visual Prompting: Robustness Inheritance and Beyond cites this paper.

Exploring Visual Prompting: Robustness Inheritance and Beyond Ensemble Adversarial Training: Attacks and Defenses

Reference 42

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Observation 1496df24-8ef4-42b9-ac56-5a8ab9ef9139 · inbound

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World? cites this paper.

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World? Ensemble Adversarial Training: Attacks and Defenses

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:20:08.256508Z

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Observation 2fb10254-2d2a-456a-99e0-b5a014516c29 · inbound

Boosting Adversarial Transferability Against Defenses via Multi-Scale Transformation cites this paper.

Boosting Adversarial Transferability Against Defenses via Multi-Scale Transformation Ensemble Adversarial Training: Attacks and Defenses

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:46:44.645596Z

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Observation 5123c869-efcf-4be5-b141-c88b49df9fec · inbound

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training cites this paper.

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training Ensemble Adversarial Training: Attacks and Defenses

Reference 75

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Observation af883479-0476-4a8a-96a6-4bc37fb667cc · inbound

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation cites this paper.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Ensemble Adversarial Training: Attacks and Defenses

Reference 23

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Observation 8af4d71c-6a3a-4efd-85c6-4a45efedc56e · inbound

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation cites this paper.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Ensemble Adversarial Training: Attacks and Defenses

Reference 59

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Observation 6e8ad9be-f1a7-4bed-a3da-dd00d0fd8c4a · inbound

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition cites this paper.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Ensemble Adversarial Training: Attacks and Defenses

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.157008Z

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Observation 50223b02-2745-4288-86e4-5ad36dbf3785 · inbound

DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment cites this paper.

DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment Ensemble Adversarial Training: Attacks and Defenses

Reference 4979

Resolution: malformed identifier; no resolver link, observed 2026-08-04T06:50:47.972317Z

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Observation bea8a810-5066-4387-924e-5d09a3e1c032 · inbound

Enhancing Adversarial Transferability through Block Stretch and Shrink cites this paper.

Enhancing Adversarial Transferability through Block Stretch and Shrink Ensemble Adversarial Training: Attacks and Defenses

Reference 33

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Observation e6c3b071-6b86-44a5-aac4-8eed3b7db475 · inbound

Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings cites this paper.

Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings Ensemble Adversarial Training: Attacks and Defenses

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:19:00.699702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44a93510-9cbb-467d-a94b-3baa9d2595b4 · inbound

REVERB-FL: Server-Side Adversarial and Reserve-Enhanced Federated Learning for Robust Audio Classification cites this paper.

REVERB-FL: Server-Side Adversarial and Reserve-Enhanced Federated Learning for Robust Audio Classification Ensemble Adversarial Training: Attacks and Defenses

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T16:40:22.607783Z

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Observation 2187b2e8-1245-4e20-9c46-06d2570aeb06 · inbound

Compression as an Adversarial Amplifier Through Decision Space Reduction cites this paper.

Compression as an Adversarial Amplifier Through Decision Space Reduction Ensemble Adversarial Training: Attacks and Defenses

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:20:57.579782Z

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Observation d1813063-dc8d-4e79-98d4-35cbe7905d48 · inbound

Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models cites this paper.

Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models Ensemble Adversarial Training: Attacks and Defenses

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:11:00.416113Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ab9852a-fe21-4f85-aa9c-4ba4f603c1d9 · inbound

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems cites this paper.

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems Ensemble Adversarial Training: Attacks and Defenses

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:51:08.883946Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02c859eb-a213-4139-835e-13aaa49a2fa4 · inbound

When AI reviews science: Can we trust the referee? cites this paper.

When AI reviews science: Can we trust the referee? Ensemble Adversarial Training: Attacks and Defenses

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-08T23:19:29.795058Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b058ae9-e4df-4862-8f08-a2a66f030170 · inbound

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach cites this paper.

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach Ensemble Adversarial Training: Attacks and Defenses

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T01:51:13.932899Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c75e075a-334f-4599-a74a-1e212525fffa · inbound

Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing cites this paper.

Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing Ensemble Adversarial Training: Attacks and Defenses

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:51:27.489190Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f12a7dc1-cb66-4ac0-ade2-5ee86a4fb368 · inbound

Margin-Adaptive Confidence Ranking for Reliable LLM Judgement cites this paper.

Margin-Adaptive Confidence Ranking for Reliable LLM Judgement Ensemble Adversarial Training: Attacks and Defenses

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T16:12:39.524794Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da90b048-6f43-4901-80eb-064a7334b2f0 · inbound

Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms cites this paper.

Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms Ensemble Adversarial Training: Attacks and Defenses

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T06:36:43.884209Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cfca49be-b075-4913-9153-669d6b25a136 · inbound

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers cites this paper.

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers Ensemble Adversarial Training: Attacks and Defenses

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-13T02:43:47.779443Z

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Unavailable: canonical work link unavailable.