Preparation of hydrophobic fabrics and effect of fluorine monomers on surface properties

Preparation of hydrophobic cotton fabric based on the self-assembly method was proposed. The cotton fabric was modified with 3-(methacryloyloxy)propyltrimethoxysilane and grafted with trifluoroethyl methacrylate and dodecafluoroheptyl methacrylate through free radical polymerization reaction. The objective of this research work was to investigate the effect of fluorine monomer with different chemical structure deposited on cotton fabric on the hydrophobic property. The chemical structure, surface topography, and surface wettability of the fabrics were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and water contact angle experiments, respectively. The results showed that the as-prepared fabrics exhibited water contact angle of above 140°. It was noticed that the fluorocarbon chain length of a modifier and its chemical structure could strongly affect the hydrophobic property of the modified fabrics, and the increase in fluorine atoms caused an increase in the water contact angle valuesPostprint (published version

Backdoor Attacks on Crowd Counting

Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks.Comment: To appear in ACMMM 2022. 10pages, 6 figures and 2 table

Preparation of hydrophobic fabrics and effect of fluorine monomers on surface properties

Preparation of hydrophobic cotton fabric based on the self-assembly method was proposed. The cotton fabric was modified with 3-(methacryloyloxy)propyltrimethoxysilane and grafted with trifluoroethyl methacrylate and dodecafluoroheptyl methacrylate through free radical polymerization reaction. The objective of this research work was to investigate the effect of fluorine monomer with different chemical structure deposited on cotton fabric on the hydrophobic property. The chemical structure, surface topography, and surface wettability of the fabrics were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and water contact angle experiments, respectively. The results showed that the as-prepared fabrics exhibited water contact angle of above 140°. It was noticed that the fluorocarbon chain length of a modifier and its chemical structure could strongly affect the hydrophobic property of the modified fabrics, and the increase in fluorine atoms caused an increase in the water contact angle value