  Semantic communications have gained significant attention as a promising
approach to address the transmission bottleneck, especially with the continuous
development of 6G techniques. Distinct from the well investigated physical
channel impairments, this paper focuses on semantic impairments in image,
particularly those arising from adversarial perturbations. Specifically, we
propose a novel metric for quantifying the intensity of semantic impairment and
develop a semantic impairment dataset. Furthermore, we introduce a deep
learning enabled semantic communication system, termed as DeepSC-RI, to enhance
the robustness of image transmission, which incorporates a multi-scale semantic
extractor with a dual-branch architecture for extracting semantics with varying
granularity, thereby improving the robustness of the system. The fine-grained
branch incorporates a semantic importance evaluation module to identify and
prioritize crucial semantics, while the coarse-grained branch adopts a
hierarchical approach for capturing the robust semantics. These two streams of
semantics are seamlessly integrated via an advanced cross-attention-based
semantic fusion module. Experimental results demonstrate the superior
performance of DeepSC-RI under various levels of semantic impairment intensity.
