  Deep learning algorithms have become an essential component in the field of
cognitive radio, especially playing a pivotal role in automatic modulation
classification. However, Deep learning also present risks and vulnerabilities.
Despite their outstanding classification performance, they exhibit fragility
when confronted with meticulously crafted adversarial examples, posing
potential risks to the reliability of modulation recognition results.
Addressing this issue, this letter pioneers the development of an intelligent
modulation classification framework based on conformal theory, named the
Conformal Shield, aimed at detecting the presence of adversarial examples in
unknown signals and assessing the reliability of recognition results. Utilizing
conformal mapping from statistical learning theory, introduces a
custom-designed Inconsistency Soft-solution Set, enabling multiple validity
assessments of the recognition outcomes. Experimental results demonstrate that
the Conformal Shield maintains robust detection performance against a variety
of typical adversarial sample attacks in the received signals under different
perturbation-to-signal power ratio conditions.
