QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis
Deep learning has dramatically advanced computer vision tasks, including person re-identification (re-ID), substantially improving matching individuals across diverse camera views. However, person re-ID systems remain vulnerable to adversarial attacks that introduce imperceptible perturbations, lead...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/22/3508 |
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| author | Eun Gi Lee Chi Hyeok Min Seok Bong Yoo |
| author_facet | Eun Gi Lee Chi Hyeok Min Seok Bong Yoo |
| author_sort | Eun Gi Lee |
| collection | DOAJ |
| description | Deep learning has dramatically advanced computer vision tasks, including person re-identification (re-ID), substantially improving matching individuals across diverse camera views. However, person re-ID systems remain vulnerable to adversarial attacks that introduce imperceptible perturbations, leading to misidentification and undermining system reliability. This paper addresses the challenge of robust person re-ID in the presence of adversarial examples by estimating attack intensity to enable effective detection and adaptive purification. The proposed approach leverages the observation that adversarial examples in retrieval tasks disrupt the relevance and internal consistency of retrieval results, degrading re-ID accuracy. This approach estimates the attack intensity and dynamically adjusts the purification strength by analyzing the query response data, addressing the limitations of fixed purification methods. This approach also preserves the performance of the model on clean data by avoiding unnecessary manipulation while improving the robustness of the system and its reliability in the presence of adversarial examples. The experimental results demonstrate that the proposed method effectively detects adversarial examples and estimates the attack intensity through query response analysis. This approach enhances purification performance when integrated with adversarial purification techniques in person re-ID systems. |
| format | Article |
| id | doaj-art-32c44cd075a846c2bd6e346df8be260f |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-32c44cd075a846c2bd6e346df8be260f2024-11-26T18:11:39ZengMDPI AGMathematics2227-73902024-11-011222350810.3390/math12223508QuEst: Adversarial Attack Intensity Estimation via Query Response AnalysisEun Gi Lee0Chi Hyeok Min1Seok Bong Yoo2Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of KoreaDeep learning has dramatically advanced computer vision tasks, including person re-identification (re-ID), substantially improving matching individuals across diverse camera views. However, person re-ID systems remain vulnerable to adversarial attacks that introduce imperceptible perturbations, leading to misidentification and undermining system reliability. This paper addresses the challenge of robust person re-ID in the presence of adversarial examples by estimating attack intensity to enable effective detection and adaptive purification. The proposed approach leverages the observation that adversarial examples in retrieval tasks disrupt the relevance and internal consistency of retrieval results, degrading re-ID accuracy. This approach estimates the attack intensity and dynamically adjusts the purification strength by analyzing the query response data, addressing the limitations of fixed purification methods. This approach also preserves the performance of the model on clean data by avoiding unnecessary manipulation while improving the robustness of the system and its reliability in the presence of adversarial examples. The experimental results demonstrate that the proposed method effectively detects adversarial examples and estimates the attack intensity through query response analysis. This approach enhances purification performance when integrated with adversarial purification techniques in person re-ID systems.https://www.mdpi.com/2227-7390/12/22/3508computer visiondeep learningadversarial metric attackperson re-identificationattack intensity estimation |
| spellingShingle | Eun Gi Lee Chi Hyeok Min Seok Bong Yoo QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis Mathematics computer vision deep learning adversarial metric attack person re-identification attack intensity estimation |
| title | QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis |
| title_full | QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis |
| title_fullStr | QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis |
| title_full_unstemmed | QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis |
| title_short | QuEst: Adversarial Attack Intensity Estimation via Query Response Analysis |
| title_sort | quest adversarial attack intensity estimation via query response analysis |
| topic | computer vision deep learning adversarial metric attack person re-identification attack intensity estimation |
| url | https://www.mdpi.com/2227-7390/12/22/3508 |
| work_keys_str_mv | AT eungilee questadversarialattackintensityestimationviaqueryresponseanalysis AT chihyeokmin questadversarialattackintensityestimationviaqueryresponseanalysis AT seokbongyoo questadversarialattackintensityestimationviaqueryresponseanalysis |