  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.
