Enhancing targeted transferability via feature space fine-tuning Article Swipe
Hui Zeng
,
Biwei Chen
,
Anjie Peng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.02727
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.02727
Adversarial examples (AEs) have been extensively studied due to their potential for privacy protection and inspiring robust neural networks. Yet, making a targeted AE transferable across unknown models remains challenging. In this paper, to alleviate the overfitting dilemma common in an AE crafted by existing simple iterative attacks, we propose fine-tuning it in the feature space. Specifically, starting with an AE generated by a baseline attack, we encourage the features conducive to the target class and discourage the features to the original class in a middle layer of the source model. Extensive experiments demonstrate that only a few iterations of fine-tuning can boost existing attacks' targeted transferability nontrivially and universally. Our results also verify that the simple iterative attacks can yield comparable or even better transferability than the resource-intensive methods, which rest on training target-specific classifiers or generators with additional data. The code is available at: github.com/zengh5/TA_feature_FT.
Related Topics
Concepts
Overfitting
Computer science
Transferability
Feature (linguistics)
Machine learning
Fidelity
Simple (philosophy)
Class (philosophy)
Artificial intelligence
Source code
Code (set theory)
Feature vector
Fine-tuning
Data mining
Artificial neural network
Set (abstract data type)
Quantum mechanics
Linguistics
Logit
Epistemology
Physics
Programming language
Philosophy
Telecommunications
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.02727
- https://arxiv.org/pdf/2401.02727
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390690167
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390690167Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.02727Digital Object Identifier
- Title
-
Enhancing targeted transferability via feature space fine-tuningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-05Full publication date if available
- Authors
-
Hui Zeng, Biwei Chen, Anjie PengList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.02727Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.02727Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.02727Direct OA link when available
- Concepts
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Overfitting, Computer science, Transferability, Feature (linguistics), Machine learning, Fidelity, Simple (philosophy), Class (philosophy), Artificial intelligence, Source code, Code (set theory), Feature vector, Fine-tuning, Data mining, Artificial neural network, Set (abstract data type), Quantum mechanics, Linguistics, Logit, Epistemology, Physics, Programming language, Philosophy, Telecommunications, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.space. | 55 |
| abstract_inverted_index.target | 73 |
| abstract_inverted_index.verify | 113 |
| abstract_inverted_index.attack, | 65 |
| abstract_inverted_index.attacks | 118 |
| abstract_inverted_index.crafted | 42 |
| abstract_inverted_index.dilemma | 37 |
| abstract_inverted_index.feature | 54 |
| abstract_inverted_index.privacy | 12 |
| abstract_inverted_index.propose | 49 |
| abstract_inverted_index.remains | 28 |
| abstract_inverted_index.results | 111 |
| abstract_inverted_index.studied | 6 |
| abstract_inverted_index.unknown | 26 |
| abstract_inverted_index.attacks' | 104 |
| abstract_inverted_index.attacks, | 47 |
| abstract_inverted_index.baseline | 64 |
| abstract_inverted_index.examples | 1 |
| abstract_inverted_index.existing | 44, 103 |
| abstract_inverted_index.features | 69, 78 |
| abstract_inverted_index.methods, | 129 |
| abstract_inverted_index.original | 81 |
| abstract_inverted_index.starting | 57 |
| abstract_inverted_index.targeted | 22, 105 |
| abstract_inverted_index.training | 133 |
| abstract_inverted_index.Extensive | 91 |
| abstract_inverted_index.alleviate | 34 |
| abstract_inverted_index.available | 144 |
| abstract_inverted_index.conducive | 70 |
| abstract_inverted_index.encourage | 67 |
| abstract_inverted_index.generated | 61 |
| abstract_inverted_index.inspiring | 15 |
| abstract_inverted_index.iterative | 46, 117 |
| abstract_inverted_index.networks. | 18 |
| abstract_inverted_index.potential | 10 |
| abstract_inverted_index.additional | 139 |
| abstract_inverted_index.comparable | 121 |
| abstract_inverted_index.discourage | 76 |
| abstract_inverted_index.generators | 137 |
| abstract_inverted_index.iterations | 98 |
| abstract_inverted_index.protection | 13 |
| abstract_inverted_index.Adversarial | 0 |
| abstract_inverted_index.classifiers | 135 |
| abstract_inverted_index.demonstrate | 93 |
| abstract_inverted_index.experiments | 92 |
| abstract_inverted_index.extensively | 5 |
| abstract_inverted_index.fine-tuning | 50, 100 |
| abstract_inverted_index.overfitting | 36 |
| abstract_inverted_index.challenging. | 29 |
| abstract_inverted_index.nontrivially | 107 |
| abstract_inverted_index.transferable | 24 |
| abstract_inverted_index.universally. | 109 |
| abstract_inverted_index.Specifically, | 56 |
| abstract_inverted_index.target-specific | 134 |
| abstract_inverted_index.transferability | 106, 125 |
| abstract_inverted_index.resource-intensive | 128 |
| abstract_inverted_index.github.com/zengh5/TA_feature_FT. | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile |