Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design | Paper Citation Record

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This paper cites Machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Machine unlearning

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This paper cites A Survey of Machine Unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design A Survey of Machine Unlearning

Reference 2

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This paper cites Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition

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This paper cites Rethinking Machine Unlearning for Large Language Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Rethinking Machine Unlearning for Large Language Models

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This paper cites The right to delete.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design The right to delete

Reference 5

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This paper cites Towards making systems forget with machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Towards making systems forget with machine unlearning

Reference 6

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This paper cites Ensuring user privacy and model security via machine unlearning: A review.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Ensuring user privacy and model security via machine unlearning: A review

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This paper cites Avoiding Copyright Infringement via Large Language Model Unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 8

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This paper cites Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models

Reference 9

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This paper cites A data-based perspec- tive on transfer learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design A data-based perspec- tive on transfer learning

Reference 10

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This paper cites Model sparsity can simplify machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Model sparsity can simplify machine unlearning

Reference 11

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Observation fceb6f4c-a4fa-41c4-a96b-c6eea28977dd · outbound

This paper cites Backdoor Defense with Machine Unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Backdoor Defense with Machine Unlearning

Reference 12

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This paper cites Un- learning backdoor attacks in federated learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Un- learning backdoor attacks in federated learning

Reference 13

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Observation b1d95bbd-d300-427e-ad99-332c186712eb · outbound

This paper cites Towards Safer Large Language Models through Machine Unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Towards Safer Large Language Models through Machine Unlearning

Reference 14

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Observation 6ce42170-8e93-4e40-a14b-daaa5cc4e906 · outbound

This paper cites Erasing Concepts from Diffusion Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Erasing Concepts from Diffusion Models

Reference 15

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This paper cites Defensive unlearning with adversarial training for robust concept erasure in diffusion models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Defensive unlearning with adversarial training for robust concept erasure in diffusion models

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This paper cites Machine unlearning: Solutions and challenges.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Machine unlearning: Solutions and challenges

Reference 17

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Observation 8c34ea07-cfe9-4e95-ab51-78cdcf9bba52 · outbound

This paper cites Machine Unlearning in Generative AI: A Survey.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Machine Unlearning in Generative AI: A Survey

Reference 18

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Observation ad13b3e5-2b73-49e0-9d04-86de7e1c4476 · outbound

This paper cites Approximate data deletion from machine learning models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Approximate data deletion from machine learning models

Reference 19

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Observation 193e73b7-9752-4df2-baa6-113befac66d6 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 20

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Observation 2e27fd56-4a96-4d59-930d-c1ac1ba4d62b · outbound

This paper cites Evaluating Machine Unlearning via Epistemic Uncertainty.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Evaluating Machine Unlearning via Epistemic Uncertainty

Reference 21

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This paper cites Unrolling sgd: Understanding factors in- fluencing machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Unrolling sgd: Understanding factors in- fluencing machine unlearning

Reference 22

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Observation 0ab6aca6-aa17-4db2-8330-da80ebc131a8 · outbound

This paper cites Machine Unlearning of Features and Labels.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Machine Unlearning of Features and Labels

Reference 23

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Observation bd9a5c9e-aabd-42cf-9453-0d570b0b0808 · outbound

This paper cites Towards unbounded machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Towards unbounded machine unlearning

Reference 24

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Observation 3f3e6ac7-a33d-4d7b-939b-549776d952c3 · outbound

This paper cites SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

Reference 25

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Observation 50c67a31-d195-4704-863a-b42fbfa0147a · outbound

This paper cites Digital watermarking and its application in image copyright protection.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Digital watermarking and its application in image copyright protection

Reference 26

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Observation 6570aa76-642d-480c-9a55-048ebaa844a6 · outbound

This paper cites Editguard: Versatile image watermarking for tamper localization and copyright protection.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Editguard: Versatile image watermarking for tamper localization and copyright protection

Reference 27

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Observation f85bf48d-a908-44fa-9c43-f66d35c20ad4 · outbound

This paper cites Digital image watermarking using deep learning: A survey.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Digital image watermarking using deep learning: A survey

Reference 28

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Observation 4a5c3a36-6ea2-47ef-8525-30fdc8902928 · outbound

This paper cites Combinational image wa- termarking in the spatial and frequency domains.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Combinational image wa- termarking in the spatial and frequency domains

Reference 29

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Observation 00866bca-379a-45cb-ad4b-63054a0db9d8 · outbound

This paper cites Visual attention-based image watermarking.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual attention-based image watermarking

Reference 30

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Observation deb3261b-e3dd-408f-89e0-9c0f410d1b47 · outbound

This paper cites Hidden: Hiding data with deep networks.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Hidden: Hiding data with deep networks

Reference 31

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Observation 1342c5d3-63bd-4cad-80e3-ce1754ae58c4 · outbound

This paper cites Distortion agnostic deep watermarking.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Distortion agnostic deep watermarking

Reference 32

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Observation eeb5a64e-9275-4bc8-914e-81f0948d850c · outbound

This paper cites Digital image watermarking using deep learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Digital image watermarking using deep learning

Reference 33

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This paper cites A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking

Reference 34

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This paper cites Making ai forget you: Data deletion in machine learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Making ai forget you: Data deletion in machine learning

Reference 35

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Observation f8848ae7-cfb0-4d8f-8859-b58e8a0f2aba · outbound

This paper cites Descent-to-delete: Gradient-based methods for machine un- learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Descent-to-delete: Gradient-based methods for machine un- learning

Reference 36

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This paper cites Remember what you want to for- get: Algorithms for machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Remember what you want to for- get: Algorithms for machine unlearning

Reference 37

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Observation 3afc88d8-bb93-4004-b02a-a74a6a103a2e · outbound

This paper cites Machine unlearning via algorithmic stability.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Machine unlearning via algorithmic stability

Reference 38

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This paper cites On the necessity of auditable algorithmic definitions for machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design On the necessity of auditable algorithmic definitions for machine unlearning

Reference 39

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Observation 8e78742a-319d-4d61-84f0-8f225a595da1 · outbound

This paper cites Our data, ourselves: Privacy via distributed noise generation.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Our data, ourselves: Privacy via distributed noise generation

Reference 40

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This paper cites Amnesiac machine learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Amnesiac machine learning

Reference 41

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Observation 5913bc4a-eb20-416d-8a22-085e0e80a89e · outbound

This paper cites Certified Data Removal from Machine Learning Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Certified Data Removal from Machine Learning Models

Reference 42

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Observation ce14a14f-a0a8-440e-8ff0-2c1c95c9be2d · outbound

This paper cites Unified concept editing in diffusion models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Unified concept editing in diffusion models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.749401Z

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This paper cites Selective amnesia: A continual learning approach to forgetting in deep generative models, 2023.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Selective amnesia: A continual learning approach to forgetting in deep generative models, 2023

Reference 44

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This paper cites Ablating concepts in text-to-image diffusion models, 2023.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Ablating concepts in text-to-image diffusion models, 2023

Reference 45

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Observation e19c5f8b-103f-4940-a5c4-ae179cf05df9 · outbound

This paper cites Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models

Reference 46

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Observation d0bd582b-deb6-4e09-8e4c-4e489b6f4efb · outbound

This paper cites Fast federated machine unlearning with nonlinear functional theory.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Fast federated machine unlearning with nonlinear functional theory

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.724639Z

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Observation 7b4990d7-2264-494c-bd6d-3b07e9d14a46 · outbound

This paper cites Federated unlearning via class-discriminative pruning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Federated unlearning via class-discriminative pruning

Reference 48

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This paper cites Federated unlearning: Guarantee the right of clients to forget.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Federated unlearning: Guarantee the right of clients to forget

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.708968Z

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Observation b8d4f8b0-9abe-45e4-993e-a8c049c492b5 · outbound

This paper cites Who’s harry potter? approximate unlearning in llms, 2023.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Who’s harry potter? approximate unlearning in llms, 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.699819Z

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This paper cites DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

Reference 51

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This paper cites Large Language Model Unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Large Language Model Unlearning

Reference 52

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This paper cites Unlearning bias in language models by partitioning gradients.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Unlearning bias in language models by partitioning gradients

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.690063Z

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Observation 1b2932aa-8cab-4929-a814-3000f8eed450 · outbound

This paper cites Morgan kaufmann, 2007.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Morgan kaufmann, 2007

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.681004Z

Source-reported events for the cited work

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Observation 84527c92-0335-4a9b-a679-de235a4e13e7 · outbound

This paper cites Informed embedding: exploiting image and detector infor- mation during watermark insertion.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Informed embedding: exploiting image and detector infor- mation during watermark insertion

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.672390Z

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Observation 16cc8073-557d-454e-8305-73af283d999f · outbound

This paper cites Attacks on digital wa- termarks: classification, estimation based attacks, and bench- marks.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Attacks on digital wa- termarks: classification, estimation based attacks, and bench- marks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.663274Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81b3fc10-d130-4c64-8dad-f2df8b727ee8 · outbound

This paper cites Dct-based watermark recovering without resort- ing to the uncorrupted original image.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Dct-based watermark recovering without resort- ing to the uncorrupted original image

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.654474Z

Source-reported events for the cited work

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Observation 369c70e5-2878-4a12-ae3e-1da5c82d240e · outbound

This paper cites A dct-domain system for robust image watermark- ing.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design A dct-domain system for robust image watermark- ing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.645480Z

Source-reported events for the cited work

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Observation acad54b6-4357-44f3-a6b8-fa2991e68c15 · outbound

This paper cites A multiresolution watermark for digital images.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design A multiresolution watermark for digital images

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.636077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8409eeba-9895-4507-b8ab-2dbb510ce31e · outbound

This paper cites An svd-based watermarking scheme for protecting rightful ownership.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design An svd-based watermarking scheme for protecting rightful ownership

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.627600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2f005051-51c5-477b-ae96-21ab73d2d225 · outbound

This paper cites SteganoGAN: High Capacity Image Steganography with GANs.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design SteganoGAN: High Capacity Image Steganography with GANs

Reference 61

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Source-reported events for the cited work

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Observation a11942d2-da59-4480-a5c0-786dec37e9dc · outbound

This paper cites Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.907842Z

Source-reported events for the cited work

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Observation 724fb28d-748f-4c54-b6b1-899c053c72eb · outbound

This paper cites Hide and Seek: How Does Watermarking Impact Face Recognition?.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Hide and Seek: How Does Watermarking Impact Face Recognition?

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:01:27.260151Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7148a55f-e9ee-4945-82d2-a17bde025b00 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Exploring Visual Prompts for Adapting Large-Scale Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.913858Z

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Observation f277603a-08d6-4f2d-ba92-97280a869c35 · outbound

This paper cites Visual Prompt Tuning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual Prompt Tuning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.916669Z

Source-reported events for the cited work

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Observation 6ea2c1ad-9e89-41d3-8d88-7bcff9ef0832 · outbound

This paper cites Adversarial Reprogramming of Neural Networks.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Adversarial Reprogramming of Neural Networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.919730Z

Source-reported events for the cited work

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Observation 7c9629e1-2ac3-48cf-aa0b-e14dba26bcd7 · outbound

This paper cites Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.922698Z

Source-reported events for the cited work

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Observation 34bc0786-24a1-4728-898e-17750e929df2 · outbound

This paper cites Adversarial Reprogramming of Text Classification Neural Networks.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Adversarial Reprogramming of Text Classification Neural Networks

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:01:27.116551Z

Source-reported events for the cited work

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Observation 0a089734-2254-4548-8f7d-81d5e248fc26 · outbound

This paper cites Cross- modal adversarial reprogramming.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Cross- modal adversarial reprogramming

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.618571Z

Source-reported events for the cited work

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Observation 64babf8b-4310-43e7-9617-b92c33048c48 · outbound

This paper cites Adversarial repro- gramming of pretrained neural networks for fraud detection.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Adversarial repro- gramming of pretrained neural networks for fraud detection

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.609590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d0f24d1-38b4-4850-ae04-dd36e2f2ea66 · outbound

This paper cites Fairness reprogramming.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Fairness reprogramming

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.599613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation adee0923-e364-463c-b234-d7f11e766053 · outbound

This paper cites Visual Prompting for Adversarial Robustness.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual Prompting for Adversarial Robustness

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.937062Z

Source-reported events for the cited work

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Observation fce01229-8864-4836-9991-dfae009e6de1 · outbound

This paper cites Showmaker: Creating high-fidelity 2d human video via fine-grained diffu- sion modeling.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Showmaker: Creating high-fidelity 2d human video via fine-grained diffu- sion modeling

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.590537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4be5a6ce-df0c-47ec-ad7d-c06c77d95c8f · outbound

This paper cites From Visual Prompt Learning to Zero-Shot Transfer: Mapping Is All You Need.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design From Visual Prompt Learning to Zero-Shot Transfer: Mapping Is All You Need

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.943285Z

Source-reported events for the cited work

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Observation f4ce22a5-adfe-4bee-8f3f-aad90d870149 · outbound

This paper cites Unleashing the Power of Visual Prompting At the Pixel Level.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Unleashing the Power of Visual Prompting At the Pixel Level

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.946324Z

Source-reported events for the cited work

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Observation 7ec79797-8bcd-4425-9eeb-95775520d15f · outbound

This paper cites Visual prompting for adversarial robustness.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual prompting for adversarial robustness

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.581227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 930dcebe-4299-4f62-b9e8-e9d884578b0a · outbound

This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.952561Z

Source-reported events for the cited work

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Observation b5a71d4d-3cab-48d3-b1d6-65c413dc1f20 · outbound

This paper cites Exploring the benefits of visual prompting in differen- tial privacy.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Exploring the benefits of visual prompting in differen- tial privacy

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.571698Z

Source-reported events for the cited work

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Observation 1e423bc1-5800-46a4-8854-b14ff1f80871 · outbound

This paper cites Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:01:27.065000Z

Source-reported events for the cited work

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Observation 374399d4-836c-487e-b8a5-cdb73f6c3498 · outbound

This paper cites When visual prompt tuning meets source-free domain adaptive semantic segmentation.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design When visual prompt tuning meets source-free domain adaptive semantic segmentation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.561505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba157989-acbc-4a75-b58d-5368ca18f1ef · outbound

This paper cites Vi- sual prompting reimagined: The power of activation prompts.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Vi- sual prompting reimagined: The power of activation prompts

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.541623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b3498a3-4f13-4163-9f7f-b79ac3727258 · outbound

This paper cites Diversity- aware meta visual prompting.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Diversity- aware meta visual prompting

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.528924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30a8bc7c-4676-4282-a7b0-d3948a9d2535 · outbound

This paper cites Convolutional Visual Prompt for Robust Visual Perception.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Convolutional Visual Prompt for Robust Visual Perception

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.970307Z

Source-reported events for the cited work

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Observation 3e7c1d23-e40d-4160-8290-419eb0e97e2a · outbound

This paper cites Learning to prompt for vision-language models.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Learning to prompt for vision-language models

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.519956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e86d794-ff2a-413c-b997-c1cb4c560696 · outbound

This paper cites Visual Prompting in Multimodal Large Language Models: A Survey.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Visual Prompting in Multimodal Large Language Models: A Survey

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.976295Z

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Observation fdf40d1b-4ca4-4725-a451-a8f12526e835 · outbound

This paper cites Lavip: Language-grounded visual prompting.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Lavip: Language-grounded visual prompting

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.510542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d3a309f-b016-485f-9c5f-2b0ea04d20c4 · outbound

This paper cites Attribute-to-Delete: Machine Unlearning via Datamodel Matching.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.982023Z

Source-reported events for the cited work

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Observation e595ce0d-f28d-4dac-86b2-dfdefe640eb5 · outbound

This paper cites Boundary unlearning: Rapid forgetting of deep net- works via shifting the decision boundary.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Boundary unlearning: Rapid forgetting of deep net- works via shifting the decision boundary

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.500042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f089fb29-ba1d-42ae-8621-c29af21b4705 · outbound

This paper cites An introduction to bilevel optimization: Foundations and applications in signal 11 processing and machine learning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design An introduction to bilevel optimization: Foundations and applications in signal 11 processing and machine learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.490366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dcd335c0-c0d2-452f-a443-f3add8066e9b · outbound

This paper cites Lipton, and J.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Lipton, and J

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.479537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b2710d3-70d7-4d4f-94a9-a1d2fe4e7047 · outbound

This paper cites The implicit function theorem: history, theory, and applications.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design The implicit function theorem: history, theory, and applications

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.470025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 786dee3f-8e99-4a1d-a270-1baff43567ad · outbound

This paper cites On the decision boundaries of neural networks: A tropical geometry perspective.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design On the decision boundaries of neural networks: A tropical geometry perspective

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.460700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation def79e41-d81f-4fb1-a7ce-3d56f0a27a79 · outbound

This paper cites Challenging forgets: Unveiling the worst-case forget sets in machine unlearning.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Challenging forgets: Unveiling the worst-case forget sets in machine unlearning

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.450812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7ab1b96-a26e-4bb4-8a4b-fcd50909427d · outbound

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

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design High-resolution image synthesis with latent diffusion models

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:01:27.441099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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