微调对抗鲁棒Transformer用于单图像去雾
Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing
AI总结：
本研究针对遥感单图像去雾Transformer易受对抗噪声影响的问题，提出两种轻量级微调策略，在保持性能的同时显著提升鲁棒性，并验证了其在遥感场景中的适用性。
摘要：
单图像去雾是遥感应用中的一个重要课题，它能够提升所获取图像的质量并提高目标检测的精度。然而，这类结构的可靠性尚未得到充分分析，这使其面临不可感知扰动的风险，这些扰动可能显著损害其性能。在这项工作中，我们表明最先进的图像到图像去雾Transformer容易受到对抗噪声的影响，即使仅改变1个像素，也能使PSNR降低多达2.8 dB。接下来，我们提出了两种轻量级微调策略，旨在提高预训练Transformer的鲁棒性。我们的方法在保持可比拟的干净性能的同时，显著增强了对对抗数据的防护。我们进一步展示了它们在两个遥感场景中的适用性，展示了其对分布外数据的鲁棒行为。对抗微调和攻击算法的源代码可在github.com/Vladimirescu/RobustDehazing获取。
英文摘要：
Single-image dehazing is an important topic in remote sensing applications, enhancing the quality of acquired images and increasing object detection precision. However, the reliability of such structures has not been sufficiently analyzed, which poses them to the risk of imperceptible perturbations that can significantly hinder their performance. In this work, we show that state-of-the-art image-to-image dehazing transformers are susceptible to adversarial noise, with even 1 pixel change being able to decrease the PSNR by as much as 2.8 dB. Next, we propose two lightweight fine-tuning strategies aimed at increasing the robustness of pre-trained transformers. Our methods results in comparable clean performance, while significantly increasing the protection against adversarial data. We further present their applicability in two remote sensing scenarios, showcasing their robust behavior for out-of-distribution data. The source code for adversarial fine-tuning and attack algorithms can be found at github.com/Vladimirescu/RobustDehazing.
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