Name
Title | ||
|---|---|---|
GlassMasq: Adversarial Examples Masquerading in Face Identification Systems with Feature Extractor |
Abstract | ||
|---|---|---|
Face identification systems based on deep neural networks (DNNs) have been widely introduced in various areas. The key component of the systems is a feature extractor trained to map a face image into a feature space to distinguish a large number of unspecified individuals. Several works revealed that the feature extractor is vulnerable to carefully crafted adversarial examples. These works indicate that we could manipulate the outputs of the feature extractor by perturbing a source image. However, they might require large perturbations to ensure that the perturbed image is recognized incorrectly. This raises a question: how can we craft adversarial examples which leads miss-identification into a target identity with high-confidence and small perturbation? In this paper, to obtain adversarial examples with high confidence and small perturbation, we first introduce a condition which adversarial examples against the face identification systems should satisfy, then introduce a new method called GlassMasq to create adversarial examples based on the condition. In our evaluations, we demonstrate that our generated adversarial examples can deceive the face identification systems with higher confidence and at most 62.8% smaller perturbation than existing methods. The proposed method enables us to evaluate robustness of the face identification system against adversarial examples in a more appropriate manner. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/PST47121.2019.8949019 | 2019 17th International Conference on Privacy, Security and Trust (PST) |
Keywords | Field | DocType |
Adversarial Example,Face Identification,Deep Neural Network,Security | Feature vector,Computer security,Computer science,Identification system,Robustness (computer science),Extractor,Artificial intelligence,Machine learning,Deep neural networks,Adversarial system | Conference |
ISSN | ISBN | Citations |
2574-139X | 978-1-7281-3266-2 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuya Kakizaki | 1 | 0 | 2.37 |
| Kosuke Yoshida | 2 | 0 | 1.01 |
| Tsubasa Takahashi | 3 | 0 | 0.34 |