Person re-identification using adversarial haze attack and defense: A deep learning framework
| Autor: | Mussarat Yasmin, Jamal Hussain Shah, Maryam Nisa, Muhammad Attique Khan, M. Maheswari, Muhammad Sharif, Seifedine Kadry, Shansa Kanwal |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Standard test image Channel (digital image) business.industry Computer science Deep learning Feature extraction Pattern recognition Discriminative model Control and Systems Engineering Search problem Artificial intelligence Electrical and Electronic Engineering Transfer of learning business Network model |
| Zdroj: | Computers & Electrical Engineering. 96:107542 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2021.107542 |
| Popis: | In this paper, the adversarial haze attack problem is addressed using the dark channel prior (DCP) de-hazing method. The adversarial attack affects rank-1 accuracy, where searching a target image against each test image is a specific search problem. To resolve this kind of problem, a feature fusion model is proposed to fuse handcrafted features and a pre-trained network model to obtain robust and discriminative features. The proposed model learns global features using transfer learning architecture whereas local features are obtained using the conventional method. Three pre-trained CNN models (AlexNet, ResNet, and Inception-v3) are used for feature extraction via transfer learning. The experiments are performed on publicly available datasets, achieving 68.6% accuracy in rank-1 with VIPER dataset and 79.6% accuracy with CHUK03 dataset. The proposed model enhances rank-1 accuracy of person re-identification when comparing with other state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect