Name
Abstract | ||
|---|---|---|
Image classification is an important task in today's world with many applications from socio-technical to safety-critical domains. The recent advent of Deep Neural Network (DNN) is the key behind such a wide-spread success. However, such wide adoption comes with the concerns about the reliability of these systems, as several erroneous behaviors have already been reported in many sensitive and critical circumstances. Thus, it has become crucial to rigorously test the image classifiers to ensure high reliability. Many reported erroneous cases in popular neural image classifiers appear because the models often confuse one class with another, or show biases towards some classes over others. These errors usually violate some group properties. Most existing DNN testing and verification techniques focus on per image violations and thus fail to detect such group-level confusions or biases. In this paper, we design, implement and evaluate DeepInspect, a white box testing tool, for automatically detecting confusion and bias of DNN-driven image classification applications. We evaluate DeepInspect using popular DNN-based image classifiers and detect hundreds of classification mistakes. Some of these cases are able to expose potential biases of the network towards certain populations. DeepInspect further reports many classification errors in state-of-the-art robust models. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Software Engineering | Journal |
Volume | Citations | PageRank |
abs/1905.07831 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchi Tian | 1 | 2 | 2.38 |
| Ziyuan Zhong | 2 | 3 | 2.73 |
| Vicente Ordonez | 3 | 1418 | 69.65 |
| Baishakhi Ray | 4 | 737 | 34.84 |