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相关概念视频

Overview of Transposition and Recombination02:13

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Transposons make up a significant part of genomes of various organisms. Therefore, it is believed that transposition played a major evolutionary role in speciation by changing genome sizes and modifying gene expression patterns. For example, in bacteria, transposition can lead to conferring antibiotic resistance. Movement of transposable elements within the genetic pool of pathogenic bacteria can aid in transfer of antibiotic-resistant genetic elements. In eukaryotes, transposons can carry out...
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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
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学习学习可转移的生成攻击对人重新识别.

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    此摘要是机器生成的。

    本研究引入了Meta可转移生成攻击 (MTGA),以测试人重新识别 (reid) 模型对抗对抗攻击的稳定性. MTGA显著提高了在不同模型,数据集和测试场景中对抗性示例的可转移性.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 深度学习的人重新识别 (reid) 模型对于监控至关重要,但易受对抗性攻击的影响.
    • 现有的攻击在跨域,跨模型和交叉测试场景中缺乏有效性.

    研究的目的:

    • 开发一种可靠的方法来评估人重新识别模型的现实世界的可靠性.
    • 为了提高黑子攻击中对抗性示例的可转移性.

    主要方法:

    • 建议使用超学习优化进行超可转移生成攻击 (MTGA).
    • 模仿跨模型和数据集黑子攻击,使用多种不同的重新识别模型和数据集.
    • 引入了 Perturbation Random Erasing 和 Normalization 混合策略以改善攻击的概括性.

    主要成果:

    • 在跨模型和数据集攻击中,MTGA表现出卓越的性能,在SOTA的平均mAP下降率上表现出20.0%的优势.
    • 与SOTA相比,MTGA在跨模型和数据集和测试攻击中取得了11.3%的更好的表现.
    • 该方法有效地产生了可以在各种Re-id模型和领域中转移的对抗性示例.

    结论:

    • MTGA为评估个人再识别系统的稳定性提供了一个强大的工具.
    • 拟议的战略增强了对抗性攻击的可转移性,这对于真实世界的安全评估至关重要.
    • 这项研究有助于理解和减轻人工智能驱动监控中的对抗性漏洞.