Person re-identification using adversarial haze attack and defense: A deep learning framework. (December 2021)

• Record Type: Journal Article
• Authors: Kanwal, Shansa; Shah, Jamal Hussain; Khan, Muhammad Attique; Nisa, Maryam; Kadry, Seifedine; Sharif, Muhammad; Yasmin, Mussarat; Maheswari, M.
• Is Part Of: Computers & electrical engineering. Volume 96:Part B(2021)
• Publication Date: 2021-12
• Subjects: Person re-identification -- Adversarial haze attack -- Dark channel prior (DCP) algorithm -- Feature fusion -- Deep learning -- Transfer learning -- Handcrafted model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107542
• Languages: English
• ISSNs: 0045-7906

Abstract

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.