Reduction kinetics of hematite powder in hydrogen atmosphere at moderate temperatures

Hydrogen has received much attention in the development of direct reduction of iron ores because hydrogen metallurgy is one of the effective methods to reduce CO2 emission in the iron and steel industry. In this study, the kinetic mechanism of reduction of hematite particles was studied in a hydrogen atmosphere. The phases and morphological transformation of hematite during the reduction were characterized using X-ray diffraction and scanning electron microscopy with energy dispersive spectroscopy. It was found that porous magnetite was formed, and the particles were degraded during the reduction. Finally, sintering of the reduced iron and wüstite retarded the reductive progress. The average activation energy was extracted to be 86.1 kJ/mol and 79.1 kJ/mol according to Flynn-Wall-Ozawa (FWO) and Starink methods, respectively. The reaction fraction dependent values of activation energy were suggested to be the result of multi-stage reactions during the reduction process. Furthermore, the variation of activation energy value was smoothed after heat treatment of hematite particles.(OLD) MSE-

DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints

Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model. These attacks are susceptible to backdoor detection algorithms and even visual inspection. In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms. We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can 1) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit