Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but also augments holistic perception in computer vision applications. Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system tend to attack the correspondence matching, impairing the performance of image stitching algorithms. In light of this challenge, this paper presents the first attempt to improve the robustness of image stitching against adversarial attacks. Specifically, we introduce a stitching-oriented attack~(SoA), tailored to amplify the alignment loss within overlapping regions, thereby targeting the feature matching procedure. To establish an attack resistant model, we delve into the robustness of stitching architecture and develop an adaptive adversarial training~(AAT) to balance attack resistance with stitching precision. In this way, we relieve the gap between the routine adversarial training and benign models, ensuring resilience without quality compromise. Comprehensive evaluation across real-world and synthetic datasets validate the deterioration of SoA on stitching performance. Furthermore, AAT emerges as a more robust solution against adversarial perturbations, delivering superior stitching results. Code is available at:https://github.com/Jzy2017/TRIS.

该论文首次尝试改进图像拼接算法对抗攻击的鲁棒性，通过引入针对特征匹配过程的拼接导向攻击（SoA），深入研究鲁棒性，开发了自适应对抗训练（AAT）来平衡攻击抵抗力和拼接精度。全面评估真实和合成数据集验证了SoA对拼接性能的损害，而AAT则成为更强大的解决方案对抗对抗扰动并提供卓越的拼接结果。

朝向鲁棒图像拼接：一种适应性抵抗兼容攻击的学习方法