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Backdoor attack injects malicious behavior to models such that inputsembedded with triggers are misclassified to a target label desired by theattacker. However, natural features may behave like triggers, causingmisclassification once embedded. While they are inevitable, mis-recognizingthem as injected triggers causes false warnings in backdoor scanning. Aprominent challenge is hence to distinguish natural features and injectedbackdoors. We develop a novel symmetric feature differencing method thatidentifies a smallest set of features separating two classes. A backdoor isconsidered injected if the corresponding trigger consists of features differentfrom the set of features distinguishing the victim and target classes. Weevaluate the technique on thousands of models, including both clean andtrojaned models, from the TrojAI rounds 2-4 competitions and a number of modelson ImageNet. Existing backdoor scanning techniques may produce hundreds offalse positives (i.e., clean models recognized as trojaned). Our techniqueremoves 78-100% of the false positives (by a state-of-the-art scanner ABS) witha small increase of false negatives by 0-30%, achieving 17-41% overall accuracyimprovement, and facilitates achieving top performance on the leaderboard. Italso boosts performance of other scanners. It outperforms false positiveremoval methods using L2 distance and attribution techniques. We alsodemonstrate its potential in detecting a number of semantic backdoor attacks.

EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry