Paper Citation Record · LEDGER

Improving Transferable Targeted Attacks with Feature Tuning Mixup

As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2411.15553.

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

pith.paper-citation-record.v1
2411.15553 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:16:28.254397Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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verified fuzzy41
unresolved8
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0829fa38-b4d2-4c1a-9096-ff55e1011afd · outbound

This paper cites Decision-based adversarial attacks: Reliable attacks against black-box machine learning models.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Decision-based adversarial attacks: Reliable attacks against black-box machine learning models

Reference 1

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Observation 9ce7040a-d03c-4fdb-90ed-c1e1d4286408 · outbound

This paper cites Improving the transferabil- ity of targeted adversarial examples through object-based di- verse input.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Improving the transferabil- ity of targeted adversarial examples through object-based di- verse input

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.119401Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 14584016-8da8-473f-9dae-b2a4b880f6a7 · outbound

This paper cites Introducing competition to boost the transferability of targeted adversarial examples through clean feature mixup.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Introducing competition to boost the transferability of targeted adversarial examples through clean feature mixup

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.105328Z

Source-reported events for the cited work

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Observation 11a4f566-d362-4f57-b07e-8ff036bd2d2b · outbound

This paper cites Towards evaluating the robustness of neural networks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Towards evaluating the robustness of neural networks

Reference 4

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Source-reported events for the cited work

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Observation 71decb99-f891-46ac-8854-73d62d9e45cf · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Xception: Deep learning with depthwise separable convolutions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.075164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7ccba21-6626-437b-a24c-d8b706ef9e3c · outbound

This paper cites Twins: Revisiting the design of spatial attention in vision transformers.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Twins: Revisiting the design of spatial attention in vision transformers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.058678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 90eee5bf-ddcf-4c25-b790-4a52f0ab34cb · outbound

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

Improving Transferable Targeted Attacks with Feature Tuning Mixup Advdiff: Generating unrestricted adversarial examples using diffusion models

Reference 7

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Source-reported events for the cited work

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Observation aaa6d4f7-506e-4c79-adab-d01b212a4074 · outbound

This paper cites Leavitt, Ari S.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Leavitt, Ari S

Reference 8

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d10f21f1-7cbc-4a7f-b119-146e773c28b4 · outbound

This paper cites Boosting adversarial at- tacks with momentum.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Boosting adversarial at- tacks with momentum

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.021739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4394cfd1-115a-4c81-ba49-54783620ec55 · outbound

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

Improving Transferable Targeted Attacks with Feature Tuning Mixup Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:29.008261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 32835829-465b-4fcb-a4b5-dabe3b31b6c8 · outbound

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

Improving Transferable Targeted Attacks with Feature Tuning Mixup An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T14:16:28.043246Z

Source-reported events for the cited work

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Observation 9c583e65-fa2c-4884-aefc-2980b8d860d3 · outbound

This paper cites Robust physical-world attacks on deep learning visual classification.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Robust physical-world attacks on deep learning visual classification

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.984311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation be8fedfd-daa2-4c56-af08-90295c79e915 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.969417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8da50b99-734d-43fe-997b-fdfdd81e030e · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Levit: a vision transformer in convnet’s clothing for faster inference

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.953741Z

Source-reported events for the cited work

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Observation 0fc05b1f-777b-4125-83c0-60d3efb93c6a · outbound

This paper cites Deep residual learning for image recognition.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Deep residual learning for image recognition

Reference 15

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Source-reported events for the cited work

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Observation a0fe6f77-a89b-4ddd-9fec-a5b00146f129 · outbound

This paper cites Rethinking spa- tial dimensions of vision transformers.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Rethinking spa- tial dimensions of vision transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.930259Z

Source-reported events for the cited work

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Observation 83a535b5-5ff8-44cb-9685-01ff139d9a9e · outbound

This paper cites Densely connected convolutional net- works.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Densely connected convolutional net- works

Reference 17

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Source-reported events for the cited work

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Observation 0bf78b26-4690-4862-a438-0a17ceea6442 · outbound

This paper cites Belongie, and Ser-Nam Lim.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Belongie, and Ser-Nam Lim

Reference 18

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Source-reported events for the cited work

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Observation 84e4b0eb-3026-49e2-9e63-b7e8e2b75532 · outbound

This paper cites Adversar- ial examples are not bugs, they are features.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Adversar- ial examples are not bugs, they are features

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.886050Z

Source-reported events for the cited work

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Observation d6a5ee66-2256-436c-9043-9bf48ac6f9e3 · outbound

This paper cites Feature space perturbations yield more transferable adversarial examples.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Feature space perturbations yield more transferable adversarial examples

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.872436Z

Source-reported events for the cited work

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Observation c48b6b83-2909-4c77-8513-6c0a1656e732 · outbound

This paper cites Liang, Lawrence Carin, and Yi- ran Chen.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Liang, Lawrence Carin, and Yi- ran Chen

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.856410Z

Source-reported events for the cited work

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Observation fc09299c-4705-4839-8d1f-2babcfc4b894 · outbound

This paper cites Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, and Yiran Chen.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, and Yiran Chen

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.838293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5359e9b5-7416-4166-b8a5-01f2a19babc2 · outbound

This paper cites Goodfellow, and Samy Bengio.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Goodfellow, and Samy Bengio

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.821388Z

Source-reported events for the cited work

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Observation efe120e2-57c5-462f-8a5c-06c3debb741b · outbound

This paper cites Towards transferable targeted attack.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Towards transferable targeted attack

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.800945Z

Source-reported events for the cited work

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Observation 846017e8-b36b-461d-b0c7-26534f9b8b1b · outbound

This paper cites Yet another intermediate-level attack.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Yet another intermediate-level attack

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.783843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a30affa3-1c42-4366-b441-eb21d6158fb5 · outbound

This paper cites Physical-world optical adversarial attacks on 3d face recognition.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Physical-world optical adversarial attacks on 3d face recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.768849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f731843e-c125-49ea-a24c-ad3a413acb6a · outbound

This paper cites UV-attack: Physical-world adversarial attacks on person detection via dynamic-NeRF-based UV mapping.

Improving Transferable Targeted Attacks with Feature Tuning Mixup UV-attack: Physical-world adversarial attacks on person detection via dynamic-NeRF-based UV mapping

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.754197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 05c778d7-fc5c-4840-9042-bfa00a9b25e6 · outbound

This paper cites Styless: Boosting the trans- ferability of adversarial examples.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Styless: Boosting the trans- ferability of adversarial examples

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.733437Z

Source-reported events for the cited work

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Observation 134dae42-93e8-48e1-9f0a-748c20e32f13 · outbound

This paper cites Hopcroft.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Hopcroft

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b8a6054e-284f-4ab8-a4f2-b3fb46777fce · outbound

This paper cites Delving into transferable adversarial examples and black- box attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Delving into transferable adversarial examples and black- box attacks

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 471ac6cb-9996-4467-9286-362ce0315817 · outbound

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

Improving Transferable Targeted Attacks with Feature Tuning Mixup Towards deep learning models resistant to adversarial attacks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.673940Z

Source-reported events for the cited work

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Observation 087b5999-1c7d-4643-b2ca-90ab3c1f61e3 · outbound

This paper cites On generating trans- ferable targeted perturbations.

Improving Transferable Targeted Attacks with Feature Tuning Mixup On generating trans- ferable targeted perturbations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.654825Z

Source-reported events for the cited work

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Observation 1a6141dd-eab3-48ce-a272-5026f5488cbc · outbound

This paper cites Boosting the transferability of ad- versarial attacks with reverse adversarial perturbation.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Boosting the transferability of ad- versarial attacks with reverse adversarial perturbation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.635633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d7cd5ee2-f27a-4864-86c7-9c0dfbc2c9f7 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.616491Z

Source-reported events for the cited work

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Observation be8576e7-550d-4801-b0fc-40fe80f41d03 · outbound

This paper cites Very deep convo- lutional networks for large-scale image recognition.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Very deep convo- lutional networks for large-scale image recognition

Reference 35

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Observation 4c893639-4cb0-412b-8964-98d9b712eda8 · outbound

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

Improving Transferable Targeted Attacks with Feature Tuning Mixup Rethinking the in- ception architecture for computer vision

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T14:16:28.177614Z

Source-reported events for the cited work

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Observation 1e94acd8-bbc2-4a7f-b5ce-e36528a1ec56 · outbound

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Improving Transferable Targeted Attacks with Feature Tuning Mixup Unresolved cited work

Reference 37

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Observation fc04feca-dd56-4901-9b0b-f050b71b48fe · outbound

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Improving Transferable Targeted Attacks with Feature Tuning Mixup Unresolved cited work

Reference 38

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Source-reported events for the cited work

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Observation 40d65317-3407-4393-9b9d-e151f7941e6d · outbound

This paper cites Enhancing the transferability of adversarial attacks through variance tuning.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Enhancing the transferability of adversarial attacks through variance tuning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.539880Z

Source-reported events for the cited work

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Observation fd52e727-0b3b-4f4c-aff6-037b0f8bebba · outbound

This paper cites Admix: Enhancing the transferability of adversarial attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Admix: Enhancing the transferability of adversarial attacks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.522432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7f8803aa-32cc-49b1-b6fd-888c014c2a69 · outbound

This paper cites Feature importance-aware transfer- able adversarial attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Feature importance-aware transfer- able adversarial attacks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.495046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 692f8b88-5041-48af-b0b5-3e3e943ca4cf · outbound

This paper cites Towards trans- ferable targeted adversarial examples.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Towards trans- ferable targeted adversarial examples

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.476042Z

Source-reported events for the cited work

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Observation fedc2ec4-2bab-4d75-a58f-f27b1b3475fa · outbound

This paper cites Enhancing the self-universality for transferable tar- geted attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Enhancing the self-universality for transferable tar- geted attacks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.454937Z

Source-reported events for the cited work

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Observation 2e036693-f6f5-44fa-bdff-f48c95e562f1 · outbound

This paper cites Skip connections matter: On the transferability of adversarial examples generated with resnets.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Skip connections matter: On the transferability of adversarial examples generated with resnets

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.437888Z

Source-reported events for the cited work

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Observation f5afdc18-c05e-48f6-9ff9-a8972953a61f · outbound

This paper cites Im- proving the transferability of adversarial samples with adver- sarial transformations.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Im- proving the transferability of adversarial samples with adver- sarial transformations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.416607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21cd60eb-6aff-4bae-8665-f625e1f471ac · outbound

This paper cites an unresolved cited work.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:16:28.395258Z

Source-reported events for the cited work

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Observation 7343574c-40b6-42ec-a8e1-283a43f84ebc · outbound

This paper cites Boosting transferability of targeted adversarial exam- ples via hierarchical generative networks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Boosting transferability of targeted adversarial exam- ples via hierarchical generative networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.379790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eda8d063-00b4-41f3-b96c-c256851ca2d3 · outbound

This paper cites Improv- ing adversarial transferability via neuron attribution-based attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Improv- ing adversarial transferability via neuron attribution-based attacks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.359578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31366859-97e5-4ee2-acfb-2073d3c9f17e · outbound

This paper cites On suc- cess and simplicity: A second look at transferable targeted attacks.

Improving Transferable Targeted Attacks with Feature Tuning Mixup On suc- cess and simplicity: A second look at transferable targeted attacks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:28.327097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 964a5691-1816-40c8-9c54-de2d74908a6a · outbound

This paper cites Is this image a photo of {target label}? Yes or No?.

Improving Transferable Targeted Attacks with Feature Tuning Mixup Is this image a photo of {target label}? Yes or No?

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T14:16:28.309194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.