Instance Reweighting Adversarial Training Based on Confused Label Article Swipe
Zhicong Qiu
,
Xianmin Wang
,
Huawei Ma
,
Songcao Hou
,
Jing Li
,
Zuoyong Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.038241
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.038241
Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation. To tackle this problem, this paper proposed an improved PM criterion, called confused-label-based PM (CL-PM), to measure the closeness mentioned above and reweight adversarial examples during training. Specifically, a confused label (CL) is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example. Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does, we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label. CL-PM shares a negative correlation with data vulnerability: data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight. Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories, and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2023.038241
- OA Status
- hybrid
- Cited By
- 5
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381956287
All OpenAlex metadata
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https://openalex.org/W4381956287Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/iasc.2023.038241Digital Object Identifier
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Instance Reweighting Adversarial Training Based on Confused LabelWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-01-01Full publication date if available
- Authors
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Zhicong Qiu, Xianmin Wang, Huawei Ma, Songcao Hou, Jing Li, Zuoyong LiList of authors in order
- Landing page
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https://doi.org/10.32604/iasc.2023.038241Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.32604/iasc.2023.038241Direct OA link when available
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Closeness, Computer science, Adversarial system, Margin (machine learning), Decision boundary, Artificial intelligence, Robustness (evolution), Machine learning, Measure (data warehouse), Data mining, Mathematics, Support vector machine, Mathematical analysis, Chemistry, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(CL-PM), | 127 |
| abstract_inverted_index.However, | 63 |
| abstract_inverted_index.approach | 49 |
| abstract_inverted_index.attacked | 34 |
| abstract_inverted_index.category | 86 |
| abstract_inverted_index.confused | 142 |
| abstract_inverted_index.critical | 109 |
| abstract_inverted_index.decision | 26, 61, 101 |
| abstract_inverted_index.evaluate | 193 |
| abstract_inverted_index.example. | 166 |
| abstract_inverted_index.examples | 2, 22, 81, 137 |
| abstract_inverted_index.improved | 121 |
| abstract_inverted_index.multiple | 91, 241 |
| abstract_inverted_index.negative | 212 |
| abstract_inverted_index.problem, | 116 |
| abstract_inverted_index.proposed | 119 |
| abstract_inverted_index.reliable | 235 |
| abstract_inverted_index.reweight | 135 |
| abstract_inverted_index.specific | 164 |
| abstract_inverted_index.training | 4, 247 |
| abstract_inverted_index.weights. | 40 |
| abstract_inverted_index.boundary. | 62 |
| abstract_inverted_index.closeness | 56, 131, 195, 239 |
| abstract_inverted_index.essential | 7 |
| abstract_inverted_index.improving | 10 |
| abstract_inverted_index.measuring | 54 |
| abstract_inverted_index.mentioned | 132 |
| abstract_inverted_index.networks, | 15 |
| abstract_inverted_index.promising | 48 |
| abstract_inverted_index.regarding | 240 |
| abstract_inverted_index.supported | 105 |
| abstract_inverted_index.training. | 139 |
| abstract_inverted_index.boundaries | 27, 102 |
| abstract_inverted_index.criterion, | 123 |
| abstract_inverted_index.indicating | 237 |
| abstract_inverted_index.prediction | 151 |
| abstract_inverted_index.robustness | 12 |
| abstract_inverted_index.vulnerable | 31 |
| abstract_inverted_index.Experiments | 229 |
| abstract_inverted_index.Reweighting | 0 |
| abstract_inverted_index.adversarial | 1, 136, 165, 246 |
| abstract_inverted_index.categories, | 93, 243 |
| abstract_inverted_index.considering | 169 |
| abstract_inverted_index.correlation | 213 |
| abstract_inverted_index.differences | 200 |
| abstract_inverted_index.discrepancy | 171 |
| abstract_inverted_index.distinguish | 79 |
| abstract_inverted_index.effectively | 78 |
| abstract_inverted_index.performance | 65 |
| abstract_inverted_index.probability | 42, 152, 174, 181, 199 |
| abstract_inverted_index.reweighting | 245 |
| abstract_inverted_index.accumulating | 197 |
| abstract_inverted_index.continuously | 51 |
| abstract_inverted_index.demonstrated | 230 |
| abstract_inverted_index.observation. | 112 |
| abstract_inverted_index.outperformed | 251 |
| abstract_inverted_index.Specifically, | 140 |
| abstract_inverted_index.counterparts. | 253 |
| abstract_inverted_index.misclassified | 85, 92, 185, 242 |
| abstract_inverted_index.safer/riskier | 222 |
| abstract_inverted_index.larger/smaller | 219 |
| abstract_inverted_index.smaller/larger | 227 |
| abstract_inverted_index.vulnerability: | 216 |
| abstract_inverted_index.state-of-the-art | 252 |
| abstract_inverted_index.path-independently | 53 |
| abstract_inverted_index.confused-label-based | 125 |
| abstract_inverted_index.multi-classification | 100 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.80621105 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |