Paper Citation Record · LEDGER

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment

As of 10 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2506.01511.

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

pith.paper-citation-record.v1
2506.01511 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:47:50.880083Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T04:35:51.583460Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

70 of 70 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 66912fae-f7d5-41a8-ac88-b98afdff4524 · outbound

This paper cites Alberti, and Tandri Gauksson.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Alberti, and Tandri Gauksson

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.852034Z

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Observation ac188a76-cd98-4f5e-b032-b2fcf78cbd70 · outbound

This paper cites Li, and David A.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Li, and David A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.757733Z

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Observation faeed672-61c9-4a6c-b100-0bab1b842b3d · outbound

This paper cites Training diffusion models with reinforce- ment learning.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Training diffusion models with reinforce- ment learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.684571Z

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Observation 56fdc742-cf16-48d0-9999-70f93eb32a79 · outbound

This paper cites IQA-PyTorch: Pytorch toolbox for image quality assessment.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment IQA-PyTorch: Pytorch toolbox for image quality assessment

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.612097Z

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Observation 3ee1dd4b-55c8-4fcc-a714-9dcf2a1a4b6d · outbound

This paper cites Enhancing diffusion models with text-encoder reinforcement learning.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Enhancing diffusion models with text-encoder reinforcement learning

Reference 5

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Source-reported events for the cited work

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Observation 92b443e6-3f3b-4f40-a12c-40e61de8c567 · outbound

This paper cites Diffusion models for impercepti- ble and transferable adversarial attack.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffusion models for impercepti- ble and transferable adversarial attack

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.520683Z

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Observation 30258d80-a91d-4c75-b205-09df1220e0d4 · outbound

This paper cites Training Deep Nets with Sublinear Memory Cost.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Training Deep Nets with Sublinear Memory Cost

Reference 7

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Observation 4d5504d6-16af-426e-ba65-0fce4a5e2f84 · outbound

This paper cites Advdiffuser: Natural adversarial example synthesis with diffusion models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Advdiffuser: Natural adversarial example synthesis with diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.464475Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ec4bdf0-c45f-4355-8cc3-808d13405a14 · outbound

This paper cites Content-based unrestricted ad- versarial attack.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Content-based unrestricted ad- versarial attack

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.388704Z

Source-reported events for the cited work

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Observation 5d048b4c-732d-413f-85d6-b9f9a99f7d36 · outbound

This paper cites Directly fine-tuning diffusion models on differentiable re- wards.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Directly fine-tuning diffusion models on differentiable re- wards

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.314521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95261d59-80c6-4967-9ace-b64c87039e77 · outbound

This paper cites Advdiff: Generating unrestricted adversarial examples using diffusion models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Advdiff: Generating unrestricted adversarial examples using diffusion models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.236425Z

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Observation 47ca93da-d899-4d00-9ceb-dc00ae8037c3 · outbound

This paper cites Imagenet large scale visual recognition competition 2012 (ilsvrc2012).

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Imagenet large scale visual recognition competition 2012 (ilsvrc2012)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:07.179179Z

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Observation 866a5768-c32d-4807-81c1-78171ba10219 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffusion models beat gans on image synthesis

Reference 13

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Observation 064b3f2d-f7a3-4798-a325-607568722637 · outbound

This paper cites Boosting adversarial at- tacks with momentum.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Boosting adversarial at- tacks with momentum

Reference 14

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Observation 672a4cb0-d215-4e3a-b451-44b95e6fed90 · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:06.405581Z

Source-reported events for the cited work

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Observation 5adf6fef-74ea-4b8c-be2c-6be027c23a6e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

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Observation a15a9b0e-fbda-42a3-b784-f35cceba4257 · outbound

This paper cites Re- inforcement learning for fine-tuning text-to-image diffusion models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Re- inforcement learning for fine-tuning text-to-image diffusion models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:06.260392Z

Source-reported events for the cited work

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Observation 70231c75-15fc-4630-a328-c1a157f9c8c9 · outbound

This paper cites Wichmann, and Wieland Brendel.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Wichmann, and Wieland Brendel

Reference 18

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Source-reported events for the cited work

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Observation c3f0f222-f451-49bc-88cc-703f63680102 · outbound

This paper cites Shortcut learning in deep neural networks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Shortcut learning in deep neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:06.115903Z

Source-reported events for the cited work

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Observation 09d8463b-5a15-4f08-8f4a-c62db9180356 · outbound

This paper cites Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:06.033900Z

Source-reported events for the cited work

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Observation 5f7ab34a-fc4b-4d0b-bcd9-e45c727a04e9 · outbound

This paper cites Countering adversarial images using input transformations.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Countering adversarial images using input transformations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.962763Z

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Observation 07c23bd5-39a8-4a1b-ba1b-b551e3c6e1aa · outbound

This paper cites Deep residual learning for image recognition.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Deep residual learning for image recognition

Reference 22

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Observation e29318d3-83bc-4d77-b5a4-82e18eda13ce · outbound

This paper cites Denoising diffu- sion probabilistic models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Denoising diffu- sion probabilistic models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.876957Z

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Observation c15b79d3-6ff6-44fb-bcb9-ee741a83fe4c · outbound

This paper cites Semantic adver- sarial examples.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Semantic adver- sarial examples

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.808786Z

Source-reported events for the cited work

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Observation a9a19de0-8d53-4c48-9d5f-595889a99de1 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment LoRA: Low-rank adaptation of large language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.729089Z

Source-reported events for the cited work

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Observation a36f6b59-96c6-4fcf-9e95-f790dc2bd08c · outbound

This paper cites Weinberger.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Weinberger

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.640234Z

Source-reported events for the cited work

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Observation 4033c7d6-1a35-45d3-85e2-289276858be4 · outbound

This paper cites Efficient decision-based black-box patch attacks on video recognition.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Efficient decision-based black-box patch attacks on video recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.567691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9611ed01-9704-4d67-a951-6a0c368ba813 · outbound

This paper cites Towards decision-based sparse attacks on video recognition.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Towards decision-based sparse attacks on video recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.494464Z

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Observation 6bd802cd-d1d1-4fdf-bfa7-bf7db4ac1b35 · outbound

This paper cites Exploring the 9 adversarial robustness of video object segmentation via one- shot adversarial attacks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Exploring the 9 adversarial robustness of video object segmentation via one- shot adversarial attacks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.416574Z

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Observation 6000cb86-9b10-4a62-916c-0ec23f5c5c1f · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Perceptual losses for real-time style transfer and super-resolution

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.325930Z

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Observation 9760cb8e-168b-4f92-ba34-701975db1e6a · outbound

This paper cites Ad- versarial examples in the physical world.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Ad- versarial examples in the physical world

Reference 31

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Observation 3ea8617e-a136-44a1-bd97-8cefd81c5fde · outbound

This paper cites Functional adversarial attacks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Functional adversarial attacks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.224921Z

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Observation 2d46b2ae-a7ac-4662-95f1-73593896f746 · outbound

This paper cites Perceptual adversarial robustness: Defense against unseen threat mod- els.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Perceptual adversarial robustness: Defense against unseen threat mod- els

Reference 33

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Observation 97dd4b90-7b32-4b72-891e-32f0f413551b · outbound

This paper cites Parrot: Pareto-optimal multi-reward reinforce- ment learning framework for text-to-image generation.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Parrot: Pareto-optimal multi-reward reinforce- ment learning framework for text-to-image generation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:05.090162Z

Source-reported events for the cited work

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Observation 99a45240-eaad-4472-9fe6-4aa46173448f · outbound

This paper cites Controlnet++: Improving conditional controls with efficient consistency feedback.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Controlnet++: Improving conditional controls with efficient consistency feedback

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.992652Z

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Observation cee066ff-61d6-4789-a1f8-00d63ab8e5b4 · outbound

This paper cites UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance

Reference 36

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Observation dfb36753-dc6c-453e-b6a8-dc6ac927f756 · outbound

This paper cites Yuille, and Cihang Xie.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Yuille, and Cihang Xie

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.923074Z

Source-reported events for the cited work

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Observation 61e0a788-6acf-4ad0-8dac-1350a19c4a3c · outbound

This paper cites Textcraftor: Your text encoder can be image quality controller.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Textcraftor: Your text encoder can be image quality controller

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.860735Z

Source-reported events for the cited work

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Observation 04fd1a4d-7037-4407-a05a-267e226ce8c3 · outbound

This paper cites Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 39

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Source-reported events for the cited work

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Observation 41e2e29e-57bc-41f5-8111-6d3f525fd4c5 · outbound

This paper cites Defense against adversarial attacks using high-level representation guided denoiser.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Defense against adversarial attacks using high-level representation guided denoiser

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.834808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 40fe17ee-f828-4215-9a7a-b1734e79b73c · outbound

This paper cites Alignment of dif- fusion models: Fundamentals, challenges, and future.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Alignment of dif- fusion models: Fundamentals, challenges, and future

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:47:00.669250Z

Source-reported events for the cited work

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Observation c75af83b-e102-467f-a8ba-b41525bd1d4b · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Swin transformer: Hierarchical vision transformer using shifted windows

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:47:00.705257Z

Source-reported events for the cited work

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Observation 4787b06e-44f4-4275-a3f9-990025f3df12 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Towards deep learning models resistant to adversarial attacks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.791554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87075dfa-cc98-4d77-9d58-4cd16a34c6c8 · outbound

This paper cites Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.757268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4cdc8a60-89bb-46cc-83a6-bae832459149 · outbound

This paper cites Ava: A large-scale database for aesthetic visual analysis.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Ava: A large-scale database for aesthetic visual analysis

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.727395Z

Source-reported events for the cited work

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Observation a4b721b9-d6df-4cc0-8b12-a0544a1084fb · outbound

This paper cites Diffusion models for adversarial purification.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffusion models for adversarial purification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.691081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5a794710-f273-45b3-84e7-c114c7a0c160 · outbound

This paper cites SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:47:51.044422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9966f4a-cca6-4203-8db1-7c4ffaa55f5c · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:47:49.716814Z

Source-reported events for the cited work

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Observation c7bc4ce8-9ad3-44e3-918d-16cbbd1f9d47 · outbound

This paper cites Semanticadv: Generating adver- sarial examples via attribute-conditioned image editing.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Semanticadv: Generating adver- sarial examples via attribute-conditioned image editing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.649015Z

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Observation bc87b0b6-feac-4c1c-ae70-7a6c569b985a · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Learn- ing transferable visual models from natural language super- vision

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.608923Z

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Observation 4f0a6394-e513-439f-8979-eb13dd4f3ab9 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment High-resolution image syn- thesis with latent diffusion models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.561319Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c9d8e1c-f66d-43b2-9e47-8de1e7b8fe27 · outbound

This paper cites Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.517879Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 06ea9c27-7beb-4067-ba2b-ea1c451f6875 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Proximal Policy Optimization Algorithms

Reference 53

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Observation e57f083d-db5e-4c1c-a8fa-77fbb17968dd · outbound

This paper cites Colorfool: Semantic adversarial coloriza- tion.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Colorfool: Semantic adversarial coloriza- tion

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.483861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed0b3181-c290-45c7-9a10-bc7f208a678b · outbound

This paper cites Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.447622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 43180df5-13ef-4a12-b1be-5d9161c6e9b1 · outbound

This paper cites Denois- ing diffusion implicit models.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Denois- ing diffusion implicit models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:47:50.207225Z

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Observation 2bb2840b-5dc9-43ea-a85c-690a4dded643 · outbound

This paper cites Rethinking the in- ception architecture for computer vision.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Rethinking the in- ception architecture for computer vision

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.403690Z

Source-reported events for the cited work

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Observation c7a83b7e-e6a0-4d3f-ab91-335016d93606 · outbound

This paper cites an unresolved cited work.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:48:04.369697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa40cfce-7466-4054-8bfe-3b109654cab1 · outbound

This paper cites Goodfellow, Dan Boneh, and Patrick D.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Goodfellow, Dan Boneh, and Patrick D

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.338421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc62e51d-6bb6-4a94-a1f7-9adb7145cb3e · outbound

This paper cites Diffusion model align- ment using direct preference optimization.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffusion model align- ment using direct preference optimization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.307539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14d7f804-c2d9-44a2-ac41-fdd873a27378 · outbound

This paper cites PVT v2: Improved baselines with pyramid vision transformer.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment PVT v2: Improved baselines with pyramid vision transformer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:04.275533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3fb66acb-6fd4-4e4f-bfd5-67710f17db7a · outbound

This paper cites Struc- ture invariant transformation for better adversarial transfer- ability.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Struc- ture invariant transformation for better adversarial transfer- ability

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:52.231210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74599fff-d08a-4035-a99a-7526d5dff110 · outbound

This paper cites Spatially transformed adversarial ex- amples.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Spatially transformed adversarial ex- amples

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:52.157996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2fa9c23a-5852-4e32-9392-c84456b3d8b0 · outbound

This paper cites an unresolved cited work.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:47:52.044009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c315343-3f04-4d26-b713-1104a984b7a4 · outbound

This paper cites Improving transferabil- ity of adversarial examples with input diversity.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Improving transferabil- ity of adversarial examples with input diversity

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.956449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ecadb49-7865-4a40-b4d7-3bc83eaafaa2 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.893554Z

Source-reported events for the cited work

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Observation 81c33e61-cfa2-4923-a750-6b8668bc97cb · outbound

This paper cites Diffusion-based adversarial sample generation for improved stealthiness and controllability.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffusion-based adversarial sample generation for improved stealthiness and controllability

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.821726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df7b8ffa-0880-4de4-a46a-937601d206a4 · outbound

This paper cites Natural color fool: Towards boosting black-box unrestricted attacks.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Natural color fool: Towards boosting black-box unrestricted attacks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.767361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44b7b892-89b8-47ac-af34-38f3f3cbf4a9 · outbound

This paper cites Diffmorpher: Unleashing the capability of diffu- sion models for image morphing.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Diffmorpher: Unleashing the capability of diffu- sion models for image morphing

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.572895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d466b596-bcfa-4dee-bc33-9719f100b718 · outbound

This paper cites an unresolved cited work.

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:47:51.387749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 7da8628c-176c-4c14-b5f0-663abaf92874 · inbound

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models cites this paper.

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-26T04:38:59.167224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.