标题： 在遥感中迁移学习的对抗脆弱性 显示英文标题

标题： On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing

摘要： 在遥感中广泛使用来自通用计算机视觉任务的预训练模型，显著降低了训练成本并提高了性能。 然而，这种做法也会给下游任务带来漏洞，其中公开可用的预训练模型可以作为代理来破坏下游模型。 本文提出了一种新颖的对抗性神经元操控方法，通过选择性地操控预训练模型中的单个或多个神经元生成可迁移的扰动。 与现有攻击方法不同，该方法无需领域特定信息，使其更具广泛适用性和效率。 通过针对多个脆弱神经元，扰动实现了优越的攻击性能，揭示了深度学习模型中的关键漏洞。 在多种模型和遥感数据集上的实验验证了所提方法的有效性。 这种低访问量的对抗性神经元操控技术突显了迁移学习模型中的重大安全风险，强调了在处理安全关键的遥感任务时，设计更强大防御措施的紧迫性。 显示英文摘要

摘要： The use of pretrained models from general computer vision tasks is widespread in remote sensing, significantly reducing training costs and improving performance. However, this practice also introduces vulnerabilities to downstream tasks, where publicly available pretrained models can be used as a proxy to compromise downstream models. This paper presents a novel Adversarial Neuron Manipulation method, which generates transferable perturbations by selectively manipulating single or multiple neurons in pretrained models. Unlike existing attacks, this method eliminates the need for domain-specific information, making it more broadly applicable and efficient. By targeting multiple fragile neurons, the perturbations achieve superior attack performance, revealing critical vulnerabilities in deep learning models. Experiments on diverse models and remote sensing datasets validate the effectiveness of the proposed method. This low-access adversarial neuron manipulation technique highlights a significant security risk in transfer learning models, emphasizing the urgent need for more robust defenses in their design when addressing the safety-critical remote sensing tasks.