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Paper Citation Record · LEDGER

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1710.10766.

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

pith.paper-citation-record.v1
1710.10766 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:27:50.007247Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T12:56:14.861539Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • 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 aa359947-8c1c-4070-935b-38729a75db8a · inbound

Defending Against Adversarial Iris Examples Using Wavelet Decomposition cites this paper.

Defending Against Adversarial Iris Examples Using Wavelet Decomposition PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-14T14:27:50.007247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:27:50.007247Z digest=sha256:20f018de1fba82cce9a5b88356e7baad413ecb66042551b37071edd289d6aa4c

Observation ea365cb1-745c-4c54-a56b-31bccddaefd6 · inbound

Improving Adversarial Robustness via Attention and Adversarial Logit Pairing cites this paper.

Improving Adversarial Robustness via Attention and Adversarial Logit Pairing PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-14T11:35:06.519037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T11:35:06.519037Z digest=sha256:d27c739760404a8b14f941cc95f9f14f4dc747c8965927917e0cba952f8791cc

Observation dd4f5fdb-2a52-4dae-8a9f-71e824ec5271 · inbound

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive cites this paper.

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 242

Resolution
metadata mismatch
local_arxiv, observed 2026-05-17T23:04:44.625747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-17T23:04:44.287660Z digest=sha256:499ab52a28c99df6ee39f8f39635d90ecd07aca2545c523d825a626575f512b4

Observation e28e2ba4-6cfb-44ec-849b-83638bc3d98d · inbound

Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation cites this paper.

Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T11:44:16.832595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:44:16.832595Z digest=sha256:439b557346b65f582672f79f1791ef4427a1a82b31e8e623d7894d7c62ecffbc

Observation 93895bf7-860d-4955-a753-816357b0e982 · inbound

Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction cites this paper.

Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T23:28:30.386865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:30.386865Z digest=sha256:590fd5af00a76e4597c446ad3647ceb67bb94e3bf58fe6ea0f6416a66076f13a

Observation 28b095fb-16d8-4ab9-a062-f71fadf7e4bd · inbound

Geometric Decoupling: Diagnosing the Structural Instability of Latent cites this paper.

Geometric Decoupling: Diagnosing the Structural Instability of Latent PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T09:48:48.290392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-10T05:06:44.051488Z digest=sha256:5b367e4edbc051603e06c37c337997fb306026ee0481f6bb92927c254beb489d

Observation ab4b4869-4dca-4ede-9f98-6afcbd38b1e4 · inbound

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces cites this paper.

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 56

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T12:56:14.863144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-09T12:55:34.631248Z digest=sha256:020ea3716de689f527f9e69e9b4e4ae1439236875f51ae976bc5150c9a79753f

Observation ae3302a1-186a-4f09-a8ce-412f7a986002 · inbound

Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking cites this paper.

Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T23:06:14.743502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:06:14.743502Z digest=sha256:baf6ebf0f7561c0ec7bec115a4b351e153df0f2a4cf47b617eb24f6b2c06e74a