Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
<|reference_start|>Artificial Immune System of Secure Face Recognition Against Adversarial Attacks: Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future. However, optimisation is required for insect production to realise its full potential. This can be by targeted improvement of traits of interest through selective breeding, an approach which has so far been underexplored and underutilised in insect farming. Here we present a comprehensive review of the selective breeding framework in the context of insect production. We systematically evaluate adjustments of selective breeding techniques to the realm of insects and highlight the essential components integral to the breeding process. The discussion covers every step of a conventional breeding scheme, such as formulation of breeding objectives, phenotyping, estimation of genetic parameters and breeding values, selection of appropriate breeding strategies, and mitigation of issues associated with genetic diversity depletion and inbreeding. This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology, offering an integrated view of the insect breeding research area and uniting knowledge which has previously remained scattered across diverse fields of expertise.<|reference_end|>
@article{ren2024artificial,
    title={Artificial Immune System of Secure Face Recognition Against Adversarial
  Attacks},
    author={Min Ren, Yunlong Wang, Yuhao Zhu, Yongzhen Huang, Zhenan Sun, Qi Li,
  Tieniu Tan},
    journal={International Journal of Computer Vision (IJCV), 2024},
    year={2024},
    doi={10.1007/s11263-024-02153-0},
    archivePrefix={arXiv},
    eprint={2406.18144},
    primaryClass={cs.CV}
}