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Reverse Engineering of Adversarial Samples by Leveraging Reverse Engineering of Adversarial Samples by Leveraging Patterns left by the Attacker Patterns left by the Attacker

Unknown authors
· 0 citations · 61 references

TL;DR

A large ‘Ad-versarial Identification Dataset’ (AID) is introduced, comprising over 180k adversarial samples generated with 13 popular attacks for image specific/agnostic white/black box setups and a novel framework for the RED objective is designed using a novel Trans-former based Global-LOcal Feature (GLoF) module.

Semantic Scholar
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