Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:44:16.832595Z

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 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 2026-08-09T06:31:02.800959+00:00.

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.

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.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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.