Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers
CC BY
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s00521-022-07918-7 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8325 |
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oai:localhost:PNK-83252023-04-26T03:57:39Z Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers Katharina, Hoedt Verena, Praher Arthur, Flexer black-box nature deep audio and image classifiers CC BY Given the rise of deep learning and its inherent black-box nature, the desire to interpret these systems and explain their behaviour became increasingly more prominent. The main idea of so-called explainers is to identify which features of particular samples have the most influence on a classifier’s prediction, and present them as explanations. Evaluating explainers, however, is difficult, due to reasons such as a lack of ground truth. In this work, we construct adversarial examples to check the plausibility of explanations, perturbing input deliberately to change a classifier’s prediction. This allows us to investigate whether explainers are able to detect these perturbed regions as the parts of an input that strongly influence a particular classification. Our results from the audio and image domain suggest that the investigated explainers often fail to identify the input regions most relevant for a prediction; hence, it remains questionable whether explanations are useful or potentially misleading. 2023-04-26T03:57:39Z 2023-04-26T03:57:39Z 2022 Book https://link.springer.com/article/10.1007/s00521-022-07918-7 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8325 en application/pdf Springer |
| institution |
Trường Đại học Phenikaa |
| collection |
DSpace |
| language |
English |
| topic |
black-box nature deep audio and image classifiers |
| spellingShingle |
black-box nature deep audio and image classifiers Katharina, Hoedt Verena, Praher Arthur, Flexer Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| description |
CC BY |
| format |
Book |
| author |
Katharina, Hoedt Verena, Praher Arthur, Flexer |
| author_facet |
Katharina, Hoedt Verena, Praher Arthur, Flexer |
| author_sort |
Katharina, Hoedt |
| title |
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| title_short |
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| title_full |
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| title_fullStr |
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| title_full_unstemmed |
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| title_sort |
constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers |
| publisher |
Springer |
| publishDate |
2023 |
| url |
https://link.springer.com/article/10.1007/s00521-022-07918-7 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8325 |
| work_keys_str_mv |
AT katharinahoedt constructingadversarialexamplestoinvestigatetheplausibilityofexplanationsindeepaudioandimageclassifiers AT verenapraher constructingadversarialexamplestoinvestigatetheplausibilityofexplanationsindeepaudioandimageclassifiers AT arthurflexer constructingadversarialexamplestoinvestigatetheplausibilityofexplanationsindeepaudioandimageclassifiers |
| _version_ |
1787741348434018304 |