Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:07:18.363722Z
Paper Citation Record · LEDGER
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment
As of 13 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2411.15673.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
20 of 20 outbound references displayed
- verified exact1
- verified fuzzy18
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 22cc8354-9241-43cb-b650-826a485d67b0 · outbound
This paper cites V ATT: trans- formers for multimodal self-supervised learning from raw video, audio and text.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment V ATT: trans- formers for multimodal self-supervised learning from raw video, audio and text
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.715323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 320f684f-ed65-414d-8b29-67f78e1d4313 · outbound
This paper cites Visual language maps for robot navigation.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Visual language maps for robot navigation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.627757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d4b8fff7-75ca-448b-8911-79d26e56e83c · outbound
This paper cites Align be- fore fuse: Vision and language representation learning with momentum distillation.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Align be- fore fuse: Vision and language representation learning with momentum distillation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.611384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c2f1ac1c-45e3-4ff5-8eaf-a687ed8100d6 · outbound
This paper cites Backdoor defense with ma- chine unlearning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Backdoor defense with ma- chine unlearning
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.585104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1bf2b972-e3fa-492d-830f-284bda07ea97 · outbound
This paper cites Visual classification via de- scription from large language models.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Visual classification via de- scription from large language models
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.569126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 74debac1-6d2f-4efe-904e-7222293fee62 · outbound
This paper cites Deepsweep: An evalua- tion framework for mitigating dnn backdoor attacks using data augmentation.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Deepsweep: An evalua- tion framework for mitigating dnn backdoor attacks using data augmentation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.553331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2f76c2d9-5233-4d50-8aa7-45b26db67140 · outbound
This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Hierarchical Text-Conditional Image Generation with CLIP Latents
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:07:18.324608Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de2b0e10-3a8f-4992-b6ae-ef0d3dca7ce6 · outbound
This paper cites How to train your vit? data, augmentation, and regularization in vision transformers.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment How to train your vit? data, augmentation, and regularization in vision transformers
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.525605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1ed620cc-5203-4ce9-b5d2-fe648741b603 · outbound
This paper cites Preserving se- mantic neighborhoods for robust cross-modal retrieval.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Preserving se- mantic neighborhoods for robust cross-modal retrieval
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.507999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 392d6bd5-9f0d-4a12-b3e5-7fd779d0948a · outbound
This paper cites Data poisoning attacks against federated learning systems.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Data poisoning attacks against federated learning systems
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.493933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 145791da-b677-432d-b099-8417b836afec · outbound
This paper cites Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversar- ial learning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversar- ial learning
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.478825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f6b293d9-0425-4cfc-8ac6-60be33fdf246 · outbound
This paper cites Multimodal fake news detection via clip-guided learning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Multimodal fake news detection via clip-guided learning
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.448083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 1ee4be82-6a1b-4305-b664-16740eecdb1e · outbound
This paper cites Is data cluster- ing in adversarial settings secure? In Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Secu- rity, page 87–98, New York, NY , USA, 2013a.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Is data cluster- ing in adversarial settings secure? In Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Secu- rity, page 87–98, New York, NY , USA, 2013a
Reference 2012
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.678551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9c57a195-0c2c-4209-8775-b5d7b7efd9e4 · outbound
This paper cites CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning
Reference 2014
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:07:18.412153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b120e6a1-ea11-4978-952a-cfd239354c0b · outbound
This paper cites Robust contrastive language-image pretraining against data poisoning and backdoor attacks.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Robust contrastive language-image pretraining against data poisoning and backdoor attacks
Reference 2015
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.464267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2c4d67cf-eb40-4f2e-8db8-18049c438504 · outbound
This paper cites Backdoor attack with imperceptible input and latent modification.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Backdoor attack with imperceptible input and latent modification
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.644195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c82d5844-03fb-480b-a3dd-4f5f2bbd7aa9 · outbound
This paper cites Backdoor attacks on self-supervised learning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Backdoor attacks on self-supervised learning
Reference 2020
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.538198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2a4e073c-c873-40a6-bdf8-331c20920284 · outbound
This paper cites Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.692860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a9017a94-1183-4791-a715-cbe08294a9c2 · outbound
This paper cites Anti-backdoor learning: Training clean models on poisoned data.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment Anti-backdoor learning: Training clean models on poisoned data
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.597792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 68a9d7d3-af63-4112-887a-1b7856fe504e · outbound
This paper cites org/blog/2023-03-30-vicuna, 1(2):3.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment org/blog/2023-03-30-vicuna, 1(2):3
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:07:18.662806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Pith citing papers
No inbound Pith citation observations are available.