Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-17T20:23:32.321605Z
Paper Citation Record · LEDGER
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP
As of 6 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2511.17362.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T07:47:37.572696Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
54 of 54 outbound references displayed
- verified exact1
- verified fuzzy50
- unresolved1
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation add1f076-d3ec-462e-9647-b569c75a8d97 · outbound
This paper cites Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization.Advances in Neural Infor- mation Processing Systems, 36:80396–80413.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization.Advances in Neural Infor- mation Processing Systems, 36:80396–80413
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.915569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5cbdaee2-36a2-4bdd-b715-ad102a08f041 · outbound
This paper cites Square attack: a query-efficient black-box adversarial attack via random search.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Square attack: a query-efficient black-box adversarial attack via random search
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.902141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 08273899-9ac3-47be-97d9-816c2202cdaf · outbound
This paper cites Synthesizing robust adversarial examples.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Synthesizing robust adversarial examples
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.918125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 81a725cb-7318-4e59-b260-7f46a4f003dd · outbound
This paper cites Food-101 - mining discriminative components with random forests.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Food-101 - mining discriminative components with random forests
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.912850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 649613e0-1713-4169-b290-5f6d28c184b4 · outbound
This paper cites Towards evaluating the robustness of neural networks.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Towards evaluating the robustness of neural networks
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.896667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 25b3ecda-b050-4929-b0d2-34da9d4d7d28 · outbound
This paper cites On evaluating ad- versarial robustness.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP On evaluating ad- versarial robustness
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.899302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9ff815f1-5883-48e8-9b16-eedf1835af5b · outbound
This paper cites Describing textures in the wild.2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606–3613.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Describing textures in the wild.2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606–3613
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.907451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6884252f-a2bb-4377-9a25-4bc72b2efa1c · outbound
This paper cites Ng, and Honglak Lee.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Ng, and Honglak Lee
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.904601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9b566442-dde4-40da-a29a-3c2a23e760d3 · outbound
This paper cites Certified adversarial robustness via randomized smoothing.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Certified adversarial robustness via randomized smoothing
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.823855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3bd75b73-1d22-4bd1-84ae-070164da0c08 · outbound
This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter- free attacks.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Reliable evaluation of adversarial robustness with an ensemble of diverse parameter- free attacks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.848831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3794d04f-15d4-4001-a15e-39419c27aec3 · outbound
This paper cites Em- bedding shift dissection on clip: Effects of augmentations on vlm’s representation learning.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Em- bedding shift dissection on clip: Effects of augmentations on vlm’s representation learning
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.784186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 21bacc3e-cefe-4edb-9d80-0ea9c9896b86 · outbound
This paper cites Fpt- noise: Dynamic scene-aware counterattack for test-time ad- versarial defense in vision-language models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Fpt- noise: Dynamic scene-aware counterattack for test-time ad- versarial defense in vision-language models
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.794001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7637d58a-3b96-484f-8212-87d193cef673 · outbound
This paper cites One-shot learning of object categories.IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:594–611.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP One-shot learning of object categories.IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:594–611
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.864840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6accd086-311f-452d-b230-301764e084d4 · outbound
This paper cites Diverse data augmentation with diffusions for effective test-time prompt tuning.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Diverse data augmentation with diffusions for effective test-time prompt tuning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.888570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7b78dfa1-20ad-4887-b167-44570570edd4 · outbound
This paper cites Clip- adapter: Better vision-language models with feature adapters.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Clip- adapter: Better vision-language models with feature adapters
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.789326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 68800268-b7a5-4500-909c-69fe9650e7d0 · outbound
This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Goodfellow, Jonathon Shlens, and Christian Szegedy
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.812687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 12d51452-ebd0-4758-b863-b0341d8b98a9 · outbound
This paper cites Caltech-256 object category dataset.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Caltech-256 object category dataset
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.791727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2318351d-467e-44c3-a3f8-c5d7855be626 · outbound
This paper cites Countering adversarial images using input transformations.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Countering adversarial images using input transformations
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.801772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c6c9aa23-03eb-4c74-a79b-63f97c7f2fe0 · outbound
This paper cites Dengel, and Damian Borth.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Dengel, and Damian Borth
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.846030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b6cb731f-cbbc-4ffa-ba30-534e2ffdac76 · outbound
This paper cites Adversarial examples are not bugs, they are features.Advances in neural information processing systems, 32.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Adversarial examples are not bugs, they are features.Advances in neural information processing systems, 32
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.781409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 793593f8-2f09-4649-b405-3bc332a3b130 · outbound
This paper cites 3d object representations for fine-grained categorization.2013 IEEE International Conference on Computer Vision Work- shops, pages 554–561.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP 3d object representations for fine-grained categorization.2013 IEEE International Conference on Computer Vision Work- shops, pages 554–561
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.893969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7d62cc90-cb72-460d-8170-3d9d9e2fe341 · outbound
This paper cites Learning multiple layers of features from tiny images.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Learning multiple layers of features from tiny images
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.796825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b98ebf29-1c71-43bf-9ca4-68e1a5bcd4c7 · outbound
This paper cites Tiny imagenet visual recognition challenge.CS 231N, 7(7):3.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Tiny imagenet visual recognition challenge.CS 231N, 7(7):3
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.786943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7699b717-7bd5-4947-af95-528286cc320a · outbound
This paper cites One prompt word is enough to boost adversarial robustness for pre-trained vision-language models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP One prompt word is enough to boost adversarial robustness for pre-trained vision-language models
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.859308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 97f81465-7813-43a1-abcd-44632c80fa33 · outbound
This paper cites Defense against adversarial at- tacks using high-level representation guided denoiser.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Defense against adversarial at- tacks using high-level representation guided denoiser
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.807330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 817835e5-ade5-4112-8933-a9b20b52c71e · outbound
This paper cites Delving into the pixels of adversarial sam- ples.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Delving into the pixels of adversarial sam- ples
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.910463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 13dee77f-1500-4181-9d27-9742012d9ca2 · outbound
This paper cites Self-calibrated consistency can fight back for adversarial robustness in vision-language models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Self-calibrated consistency can fight back for adversarial robustness in vision-language models
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T20:25:11.255907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0d12289b-1731-4a74-9914-fecc88d0475b · outbound
This paper cites Safety at scale: A comprehensive survey of large model and agent safety.Foundations and Trends® in Privacy and Security, 8(3-4):254–469.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Safety at scale: A comprehensive survey of large model and agent safety.Foundations and Trends® in Privacy and Security, 8(3-4):254–469
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.809914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5d12e35a-5786-41d7-920e-2709576eabf2 · outbound
This paper cites Towards deep learn- ing models resistant to adversarial attacks.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Towards deep learn- ing models resistant to adversarial attacks
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.879436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 184b546f-a60b-43f6-9bc1-1ba87c5d3b83 · outbound
This paper cites Towards deep learning models resistant to adversarial attacks.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Towards deep learning models resistant to adversarial attacks
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.885661Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c2654aff-be8b-4ccb-a3da-2a35c26b574f · outbound
This paper cites Fine-Grained Visual Classification of Aircraft.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Fine-Grained Visual Classification of Aircraft
Reference 31
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-17T20:25:11.259422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 24778153-7d1d-42da-8bc9-3b23acff4383 · outbound
This paper cites Understanding zero-shot adversarial robust- ness for large-scale models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Understanding zero-shot adversarial robust- ness for large-scale models
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.862136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 115def3e-9dc1-4354-b574-e20da73ede1e · outbound
This paper cites Adversarial attacks are reversible with natural supervision.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Adversarial attacks are reversible with natural supervision
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.804604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e1919799-c563-469d-bf45-93ec8c24649d · outbound
This paper cites Diffusion models for adversarial purification.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Diffusion models for adversarial purification
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.882860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 32180dfe-c554-47f0-a3b0-945b8c68bf44 · outbound
This paper cites Automated flower classification over a large number of classes.2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722–729.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Automated flower classification over a large number of classes.2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722–729
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.815665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2921b389-dcae-4f4b-880b-c89da50ee4ac · outbound
This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Parkhi, Andrea Vedaldi, Andrew Zisserman, and C
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.831654Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7d9de371-c8f6-408e-a524-84aba1f18474 · outbound
This paper cites Enhancing adversarial robustness via test-time transformation ensembling.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Enhancing adversarial robustness via test-time transformation ensembling
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.826538Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 49eb4840-5db3-4910-baec-388173248c4d · outbound
This paper cites Learning transferable visual models from natural language supervision.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Learning transferable visual models from natural language supervision
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.891406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d7c60b43-6bdb-437d-99b6-59f6f1821bb6 · outbound
This paper cites Robust clip: Unsupervised adversar- ial fine-tuning of vision embeddings for robust large vision- language models.ICML.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Robust clip: Unsupervised adversar- ial fine-tuning of vision embeddings for robust large vision- language models.ICML
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.834210Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7d7798be-c0a3-4527-b103-bee1bab46f2c · outbound
This paper cites R-tpt: Improving adversarial robustness of vision-language mod- els through test-time prompt tuning.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP R-tpt: Improving adversarial robustness of vision-language mod- els through test-time prompt tuning
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.841585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 48ab3caa-7b71-4a47-bc7a-2cc4e92b5ea6 · outbound
This paper cites Test-time prompt tuning for zero-shot generalization in vision-language models.Advances in Neural Information Processing Systems, 35:14274–14289.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Test-time prompt tuning for zero-shot generalization in vision-language models.Advances in Neural Information Processing Systems, 35:14274–14289
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.856495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 83ab8333-1587-4aee-b436-eb9b8ccf0078 · outbound
This paper cites Test-time alignment-enhanced adapter for vision-language models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Test-time alignment-enhanced adapter for vision-language models
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.876674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4e951113-519e-428b-927a-9a91a1f6610d · outbound
This paper cites On adaptive attacks to adversarial example defenses.Advances in neural information processing systems, 33:1633–1645.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP On adaptive attacks to adversarial example defenses.Advances in neural information processing systems, 33:1633–1645
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.818092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9eb9bef1-beb0-4d95-98ee-8912f982bfdc · outbound
This paper cites Pre- trained model guided fine-tuning for zero-shot adversarial robustness.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Pre- trained model guided fine-tuning for zero-shot adversarial robustness
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.852138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation dac470d2-9a30-4802-aef4-73ad19bd7171 · outbound
This paper cites Tapt: Test-time adversarial prompt tuning for robust inference in vision-language models.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Tapt: Test-time adversarial prompt tuning for robust inference in vision-language models
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.829303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 85b92551-4a78-406d-8991-63c1c0e7b31e · outbound
This paper cites Clip is strong enough to fight back: Test-time counterattacks towards zero- shot adversarial robustness of clip.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Clip is strong enough to fight back: Test-time counterattacks towards zero- shot adversarial robustness of clip
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.839261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8ad5ec68-b417-445e-9424-a01f16c8c683 · outbound
This paper cites Adversarial attacks beyond the image space.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Adversarial attacks beyond the image space
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.843897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2652c6d0-10d8-4e4b-8721-5c04feb5c08e · outbound
This paper cites Clipure: Purification in latent space via clip for adversarially robust zero-shot classification.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Clipure: Purification in latent space via clip for adversarially robust zero-shot classification
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.871225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8d5bb4c4-bfa6-4931-abcc-2f10b9956350 · outbound
This paper cites Learning to prompt for vision-language models.Inter- national Journal of Computer Vision, 130:2337 – 2348.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Learning to prompt for vision-language models.Inter- national Journal of Computer Vision, 130:2337 – 2348
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.873919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8d85edd7-b0ea-4876-9d3f-be6b90d2be12 · outbound
This paper cites Revisiting the adversarial robustness of vision language mod- els: a multimodal perspective.CoRR.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Revisiting the adversarial robustness of vision language mod- els: a multimodal perspective.CoRR
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.820739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 65f33a6c-79a1-4744-97c5-36362ad7588d · outbound
This paper cites an unresolved cited work.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T20:25:11.868364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3da0cc44-67b8-441a-8eb4-2d351d2e057a · outbound
This paper cites 4.2 we argue that the augmentation-induced latent drift vectors are scattered for clean samples and consistent for adversarial inputs.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP 4.2 we argue that the augmentation-induced latent drift vectors are scattered for clean samples and consistent for adversarial inputs
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.836826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 90832453-5e39-4906-9c06-5174c1904ebe · outbound
This paper cites 5.3, we find that the effect of α is minimal while τ ∗ is crucial.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP 5.3, we find that the effect of α is minimal while τ ∗ is crucial
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T20:25:11.799250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0fa35f68-7d61-4f71-b4bc-86237b7c2b81 · outbound
This paper cites nearly hard.
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP nearly hard
Reference 54
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-17T20:25:11.252735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
Observation bf1b213c-57f8-43b4-912b-151de05a8023 · inbound
Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP cites this paper.
Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T07:47:37.572696Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.