  Segment anything model (SAM), as the name suggests, is claimed to be capable
of cutting out any object and demonstrates impressive zero-shot transfer
performance with the guidance of prompts. However, there is currently a lack of
comprehensive evaluation regarding its robustness under various corruptions.
Understanding the robustness of SAM across different corruption scenarios is
crucial for its real-world deployment. Prior works show that SAM is biased
towards texture (style) rather than shape, motivated by which we start by
investigating its robustness against style transfer, which is synthetic
corruption. Following by interpreting the effects of synthetic corruption as
style changes, we proceed to conduct a comprehensive evaluation for its
robustness against 15 types of common corruption. These corruptions mainly fall
into categories such as digital, noise, weather, and blur, and within each
corruption category, we explore 5 severity levels to simulate real-world
corruption scenarios. Beyond the corruptions, we further assess the robustness
of SAM against local occlusion and local adversarial patch attacks. To the best
of our knowledge, our work is the first of its kind to evaluate the robustness
of SAM under style change, local occlusion, and local adversarial patch
attacks. Given that patch attacks visible to human eyes are easily detectable,
we further assess its robustness against global adversarial attacks that are
imperceptible to human eyes. Overall, this work provides a comprehensive
empirical study of the robustness of SAM, evaluating its performance under
various corruptions and extending the assessment to critical aspects such as
local occlusion, local adversarial patch attacks, and global adversarial
attacks. These evaluations yield valuable insights into the practical
applicability and effectiveness of SAM in addressing real-world challenges.
