Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Robust Physical-World Attacks on Deep Learning Models
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1707.08945.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T17:27:29.241219Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T06:15:00.866473Z
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 f00ed1be-75ad-46d5-9b54-40d71e57e634 · inbound
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning cites this paper.
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning Robust Physical-World Attacks on Deep Learning Models
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T00:38:54.712524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f379adb4-ac5e-4503-aabe-1a4d8fdca135 · inbound
MobilBye: Attacking ADAS with Camera Spoofing cites this paper.
MobilBye: Attacking ADAS with Camera Spoofing Robust Physical-World Attacks on Deep Learning Models
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T17:41:05.553026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 728c894e-ec04-42c0-b4ee-a4f4301fca3c · inbound
Fooling a Real Car with Adversarial Traffic Signs cites this paper.
Fooling a Real Car with Adversarial Traffic Signs Robust Physical-World Attacks on Deep Learning Models
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T12:36:57.746855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c4c99425-ca85-4b24-9d2d-53242456eb70 · inbound
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems cites this paper.
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems Robust Physical-World Attacks on Deep Learning Models
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T22:55:02.779854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 256bc7d8-474e-448a-a218-c0c15386b974 · inbound
Connecting Lyapunov Control Theory to Adversarial Attacks cites this paper.
Connecting Lyapunov Control Theory to Adversarial Attacks Robust Physical-World Attacks on Deep Learning Models
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T20:09:52.775185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c6add0cd-8ccf-4c84-8ee8-c118b33c0110 · inbound
Open DNN Box by Power Side-Channel Attack cites this paper.
Open DNN Box by Power Side-Channel Attack Robust Physical-World Attacks on Deep Learning Models
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T18:44:49.249505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d351a387-47e9-48e5-9094-4711c8cc39e4 · inbound
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients cites this paper.
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients Robust Physical-World Attacks on Deep Learning Models
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T15:24:57.789950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0bc476e4-1c7b-4fdc-b876-559eae6ac6af · inbound
Consumer Law for AI Agents cites this paper.
Consumer Law for AI Agents Robust Physical-World Attacks on Deep Learning Models
Reference 22
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-19T04:22:02.707012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 23820165-bcf4-4726-8123-5512e86719f3 · inbound
Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks cites this paper.
Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks Robust Physical-World Attacks on Deep Learning Models
Reference 79
- Resolution
- verified exact
- local_arxiv, observed 2026-05-16T12:12:51.198419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1624044b-7e9c-4bd6-9822-bfcd7d82652b · inbound
Street-Legal Physical-World Adversarial Rim for License Plates cites this paper.
Street-Legal Physical-World Adversarial Rim for License Plates Robust Physical-World Attacks on Deep Learning Models
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T21:23:17.244118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation beabe9e2-59c6-41a4-8fba-0aa4468bd684 · inbound
AVISE: Framework for Evaluating the Security of AI Systems cites this paper.
AVISE: Framework for Evaluating the Security of AI Systems Robust Physical-World Attacks on Deep Learning Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T00:24:47.403411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 966ccf2b-c2a8-454e-8f2e-22f416dac98c · inbound
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations cites this paper.
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations Robust Physical-World Attacks on Deep Learning Models
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:01:18.266806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1ccb8ba0-f25b-49ae-8b8b-070a33305d53 · inbound
Landseer: Exploring the Machine Learning Defense Landscape cites this paper.
Landseer: Exploring the Machine Learning Defense Landscape Robust Physical-World Attacks on Deep Learning Models
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T17:33:45.450151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d7d05127-bf97-49bc-b5a5-e72d348cec98 · inbound
Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies cites this paper.
Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies Robust Physical-World Attacks on Deep Learning Models
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T05:47:41.563393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.