提升图像去雾Transformer的抗干扰能力，防止细微噪声导致性能下降。
Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing
- 通过轻量级微调增强预训练模型对抗扰动的能力。
- 仅1像素扰动即可使PSNR下降2.8 dB，微调后显著改善鲁棒性。
- 适用于遥感场景，对分布外数据表现稳健，适合实际部署。
单图像去雾在遥感应用中至关重要，能提升图像质量并增强目标检测精度。然而，现有方法的可靠性尚未充分评估，易受不可察觉的扰动影响，导致性能显著下降。本文发现，当前最先进的图像到图像去雾Transformer对对抗噪声极为敏感，仅1像素变化即可使PSNR降低高达2.8 dB。为此，我们提出两种轻量级微调策略，可在保持原有清晰图像性能的同时，显著提升对对抗样本的防御能力。进一步在两个遥感场景中验证了方法的有效性，展示了其在分布外数据上的稳健表现。相关对抗微调与攻击算法代码已开源于github.com/Vladimirescu/RobustDehazing。
原文摘要 · Abstract (English)
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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