Learning Security Classifiers with Verified Global Robustness Properties
Yizheng Chen,
Shiqi Wang,
Yue Qin
et al.
Abstract:Many recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all inputs. Since data distribution shift is very common in security applications, e.g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier. Therefore, it is more desirable to enforce global robustness properties that hold for all input… Show more
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