Paper Citation Record · LEDGER

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 3 inbound Pith citation observations for arXiv:2411.14834.

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

pith.paper-citation-record.v1
2411.14834 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:53:50.037447Z

measured 39 of 39 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:59:11.782620Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:13:23.111381Z

Reference resolution

36 of 36 outbound references displayed

verified exact3
verified fuzzy17
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e700af40-ed39-40a5-8453-da724994a0a5 · outbound

This paper cites Robustness to Adversarial Examples through an Ensemble of Specialists.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Robustness to Adversarial Examples through an Ensemble of Specialists

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:53:51.193595Z

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 6d267bd0-f9d3-4006-8124-01b0f2accdc8 · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.494441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 128d5d5d-ffc5-4cf1-affd-34fbbe6607b6 · outbound

This paper cites Evasion attacks against machine learning at test time.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Evasion attacks against machine learning at test time

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.914360Z

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 de0ff193-5166-4b4d-8b08-7651b52ba56b · outbound

This paper cites Comment on "Biologically inspired protection of deep networks from adversarial attacks".

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Comment on "Biologically inspired protection of deep networks from adversarial attacks"

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:53:51.136225Z

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 de1b09f3-ac13-423d-9476-cc5cfdd83ec7 · outbound

This paper cites Defensive Distillation is Not Robust to Adversarial Examples.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Defensive Distillation is Not Robust to Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.556161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3594dd7-b938-4b83-add6-301e7a66d2e3 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Towards evaluating the robustness of neural networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.564786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c1219a4-8011-4cb7-bf1f-f8a8fbfb0605 · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Certified adversarial robustness via randomized smoothing

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.832178Z

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 7063d669-64a8-4270-afda-8d2566bdd889 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.802270Z

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 90a6de1e-18ce-45ba-98a9-e02cf990be24 · outbound

This paper cites Mind the box: l 1-apgd for sparse adversarial attacks on image classifiers.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Mind the box: l 1-apgd for sparse adversarial attacks on image classifiers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.772450Z

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 718fd03b-459e-49b9-94dc-4274e04416b8 · outbound

This paper cites A note on implementation errors in recent adaptive attacks against multi-resolution self-ensembles, 2025.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense A note on implementation errors in recent adaptive attacks against multi-resolution self-ensembles, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.732837Z

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 21282a69-2370-4218-b347-dd4590b01900 · outbound

This paper cites Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness, 2024.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.707237Z

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 287e0bf2-e22c-4e8b-97b3-0a7c889dc73a · outbound

This paper cites Do Perceptually Aligned Gradients Imply Adversarial Robustness?.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Do Perceptually Aligned Gradients Imply Adversarial Robustness?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.636427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19664959-5658-4e6d-9b18-94517d64e1a8 · outbound

This paper cites Ai2: Safety and robustness certification of neural networks with abstract interpretation.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Ai2: Safety and robustness certification of neural networks with abstract interpretation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.648974Z

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 a26a68f9-dd56-41d3-87e9-34d545b08f6d · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Countering Adversarial Images using Input Transformations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.660015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d539ec5f-2cec-406e-bbfb-12664d330255 · outbound

This paper cites Certified robustness to adversarial examples with differential privacy.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Certified robustness to adversarial examples with differential privacy

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.620719Z

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 273752d5-9c32-4cd4-919a-de7a25591e17 · outbound

This paper cites Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.683838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd852625-9c08-46ef-abb3-833e168f1111 · outbound

This paper cites Foveation-based Mechanisms Alleviate Adversarial Examples.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Foveation-based Mechanisms Alleviate Adversarial Examples

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.695032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cddd7ea-12f3-4fcc-a0c5-7f994486b273 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Towards deep learning models resistant to adversarial attacks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.712438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03ba3dba-afd0-4dd6-b3d2-9695316903e1 · outbound

This paper cites On Detecting Adversarial Perturbations.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense On Detecting Adversarial Perturbations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.745827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0bc53ba-7838-4ec5-afa0-2298a0f46c79 · outbound

This paper cites Biologically inspired protection of deep networks from adversarial attacks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Biologically inspired protection of deep networks from adversarial attacks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.767307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7074886-5d57-462b-86de-6bd3397eaf29 · outbound

This paper cites Improving adversarial robustness via promoting ensemble diversity.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Improving adversarial robustness via promoting ensemble diversity

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.792683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bab62494-06c6-4ff3-8652-69fdb9d33746 · outbound

This paper cites Practical black-box attacks against machine learning.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Practical black-box attacks against machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.525501Z

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 2e03977d-314c-4f75-8240-f81472f9a95c · outbound

This paper cites Barrage of random transforms for adversarially robust defense.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Barrage of random transforms for adversarially robust defense

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.491245Z

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 9aa09eef-21df-489a-b3d5-5bd56ddd86e8 · outbound

This paper cites Certified defenses against adversarial examples.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Certified defenses against adversarial examples

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.444316Z

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 8f9a4fe8-443b-4040-b0b0-77db521a6432 · outbound

This paper cites Adversarial Manipulation of Deep Representations.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Adversarial Manipulation of Deep Representations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.829281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8e66328-887f-43ca-9ed4-8f94eac8163c · outbound

This paper cites Public comment: Robustness eval- uation seems invalid.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Public comment: Robustness eval- uation seems invalid

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.415034Z

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 d3d9b41d-766b-4200-9248-86cefbad637b · outbound

This paper cites Intriguing properties of neural networks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Intriguing properties of neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.388474Z

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 eaa4b932-70e0-4115-907d-e0965d3d56e5 · outbound

This paper cites On adaptive attacks to adversarial example defenses.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense On adaptive attacks to adversarial example defenses

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.357585Z

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 f6382127-2423-4069-8539-63d6dc8b49e1 · outbound

This paper cites Ensemble adversarial training: Attacks and defenses.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Ensemble adversarial training: Attacks and defenses

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.323175Z

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 db086f34-d2c6-4674-8e8f-a9891a3594fc · outbound

This paper cites Robustness May Be at Odds with Accuracy.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Robustness May Be at Odds with Accuracy

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.890355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46a9d7fe-f7a1-4db8-9775-814805734c3c · outbound

This paper cites Provable defenses against adversarial examples via the convex outer adversarial polytope.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Provable defenses against adversarial examples via the convex outer adversarial polytope

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.296416Z

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 a3659500-9e2a-4bf2-bdc1-45a26bfb4934 · outbound

This paper cites Fast is better than free: Revisiting adversarial training.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Fast is better than free: Revisiting adversarial training

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.935262Z

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Observation 17055f19-c58c-4746-97d6-4cbf3ffd4973 · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Mitigating Adversarial Effects Through Randomization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.943965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f64418-68c4-4247-a308-f041475ebbc5 · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T14:53:49.956062Z

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Unavailable: canonical work link unavailable.

Observation 5912b5f7-bb59-4425-b7c9-82380fddcf21 · outbound

This paper cites ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:53:50.185618Z

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 a9337580-12b0-4298-b0fb-c33ba03fce1a · outbound

This paper cites Increasing confidence in adversarial robustness evaluations.

Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Increasing confidence in adversarial robustness evaluations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:53:51.243245Z

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 717f7e6f-9a0e-45e0-9b1f-dbe646eb59c1 · inbound

Obfuscated Activations Bypass LLM Latent-Space Defenses cites this paper.

Obfuscated Activations Bypass LLM Latent-Space Defenses Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-11T16:59:11.782620Z

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Unavailable: canonical work link unavailable.

Observation c2e8f636-5555-402c-93d0-d36e5ea8c869 · inbound

A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles cites this paper.

A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T15:07:57.603843Z

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Unavailable: canonical work link unavailable.

Observation ff203279-d3e1-42e9-b737-42f90cc9a2e6 · inbound

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track cites this paper.

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:13:23.189698Z

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.