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Paper Citation Record · LEDGER

Torchattacks: A PyTorch Repository for Adversarial Attacks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2010.01950.

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

pith.paper-citation-record.v1
2010.01950 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:00:08.176290Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T02:37:46.246551Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 789d3e10-7335-4959-a107-12f69da58997 · inbound

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

Towards Generalized Certified Robustness with Multi-Norm Training Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:03:24.402328Z

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.

source=arxiv_source observed=2026-05-23T19:59:54.127391Z digest=sha256:dafd8808d6b3e75322c4195b73926ab41202f3c32d09bd0efc3806abc477828e

Observation bf539ec5-2705-4855-9f5d-23880946a276 · inbound

LLM-Safety Evaluations Lack Robustness cites this paper.

LLM-Safety Evaluations Lack Robustness Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:27:21.222735Z

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.

source=pdf_text observed=2026-05-23T01:26:45.402983Z digest=sha256:8435ac5a8826e52a4e1e0be722d911514fcf05e77a1670ea8809fc94a574bd01

Observation 5d43278b-2bef-4578-9fa2-5ee947091ea9 · inbound

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples cites this paper.

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T11:00:08.176290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:00:08.176290Z digest=sha256:f5947b9e29c4e82541f82b0d495affd3bb043844c289d02beb0e1953de5c6274

Observation 65cd1191-ea96-446b-92b3-91d6b426a845 · inbound

Canonical Latent Representations in Conditional Diffusion Models cites this paper.

Canonical Latent Representations in Conditional Diffusion Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T04:43:05.948566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:43:05.948566Z digest=sha256:e4905f1b5e90960920b8b91cdadf4bd9df864548468832b4387885af4f504b19

Observation 22e1d19a-6bfc-45dc-8df2-14f2c4ad474d · inbound

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models cites this paper.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T11:18:42.857844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:18:42.857844Z digest=sha256:cf8ca0d1216a18bbead157e017444ee9629c885205a9ccb4ff42331cb89c8355

Observation 618b0d31-3a12-4bb9-b533-c24a5b4da6cc · inbound

Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness cites this paper.

Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:12:37.240326Z

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.

source=pdf_text observed=2026-05-18T13:11:34.125273Z digest=sha256:4442455853a3fdf82996f591d006d12c0256c97db394b6cc270d2cffa6472a5f

Observation ff189b1d-3cf0-4a57-b4ff-484429028eae · inbound

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits cites this paper.

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T08:40:57.767629Z

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.

source=pdf_text observed=2026-05-10T16:35:01.080061Z digest=sha256:06ccdfb549175d22214b3c3e4ed2418d2ab8d43b0f9c94d19721bb588f99d7ec

Observation 5d081748-78fb-4e7a-a403-7309b68cad6d · inbound

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits cites this paper.

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-04T05:31:09.857639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:31:09.857639Z digest=sha256:4954c75100454da11826cbdb87011353132f6cd7bdbb8109d1ee63a957bcb5f7

Observation 4ed712e5-8e97-4d0b-b9fd-df92eb416cf5 · inbound

Low Rank Adaptation for Adversarial Perturbation cites this paper.

Low Rank Adaptation for Adversarial Perturbation Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:41:25.930229Z

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.

source=pdf_text observed=2026-05-07T10:02:52.801179Z digest=sha256:e7ac55e41afa6d4ceab1fec1e6906954a7dd666a2290bc794d1d4005041a4cc0

Observation 8641105a-3916-44f4-8aec-44db86dd31b4 · inbound

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models cites this paper.

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:23:21.480982Z

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.

source=pdf_text observed=2026-05-20T14:20:49.278545Z digest=sha256:2c0a3992658c8f67ebebe94d05687a5b1f2ba289e77dde070a26bddb47d451b3

Observation fbbf29ff-a0f5-4913-b97e-3e8e86e021ff · inbound

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs cites this paper.

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:26:17.823169Z

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.

source=pdf_text observed=2026-06-28T15:23:26.461740Z digest=sha256:0e4f2f1ba57a49141736de74825d81f969fcf2060e0c5f2f79d927fed35e220c

Observation fc9c9381-aefc-4ef6-9219-e776ca20813a · inbound

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP cites this paper.

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T06:44:19.664549Z

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.

source=pdf_text observed=2026-06-30T06:35:43.534963Z digest=sha256:88cc81522ea9840a817e45a8a695fa5e5a12b53790e6e3614bbc253632242ea6

Observation 1f6674bf-643d-407e-8d43-dae61902a4d4 · inbound

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP cites this paper.

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T06:45:29.238686Z

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.

source=pdf_text observed=2026-07-01T06:44:52.762530Z digest=sha256:1c2c42a339cecc77ad82c2633eab76a655ec9a484b9091f980b184b7c51fe760

Observation 98c88676-9718-44c6-92b5-138a9f3f625b · inbound

Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor cites this paper.

Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 65

Resolution
metadata mismatch
local_arxiv, observed 2026-07-11T02:37:46.271194Z

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.

source=arxiv_source observed=2026-07-11T02:37:31.302581Z digest=sha256:06b42cd393c3e6c02583e87ab5d0b9f7bfdc0b947025d55b728437e39d82e215

Observation c97a8057-dcd7-4011-8b11-ba41d0295820 · 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 Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 47

Resolution
malformed identifier
no resolver link, observed 2026-07-13T02:43:47.779443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T02:43:47.779443Z digest=sha256:a901c16ebd0f167d9831275c8634ce8c5915d0a4ef81bae345d8e3a4761f20b2

Observation 8c43f685-c089-460c-b8a5-45daad431959 · inbound

Test Case Prioritization for DNNs via Neural Collapse Instability cites this paper.

Test Case Prioritization for DNNs via Neural Collapse Instability Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-01T10:59:43.464784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:59:43.464784Z digest=sha256:9c690a128bf1680261ee7bcb667e3dbc3b3d45f30d6e660761c227894cd0a606