Person re-identification using adversarial haze attack and defense: A deep learning framework
Shansa Kanwal, Jamal Hussain Shah, Muhammad Attique Khan, Maryam Nisa, Seifedine Kadry, Muhammad Sharif, Mussarat Yasmin, M. Maheswari
Abstract
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.