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Backdoor attacks insert malicious data into a training set so that, duringinference time, it misclassifies inputs that have been patched with a backdoortrigger as the malware specified label. For backdoor attacks to bypass humaninspection, it is essential that the injected data appear to be correctlylabeled. The attacks with such property are often referred to as "clean-labelattacks." Existing clean-label backdoor attacks require knowledge of the entiretraining set to be effective. Obtaining such knowledge is difficult orimpossible because training data are often gathered from multiple sources(e.g., face images from different users). It remains a question whetherbackdoor attacks still present a real threat.  This paper provides an affirmative answer to this question by designing analgorithm to mount clean-label backdoor attacks based only on the knowledge ofrepresentative examples from the target class. With poisoning equal to or lessthan 0.5% of the target-class data and 0.05% of the training set, we can traina model to classify test examples from arbitrary classes into the target classwhen the examples are patched with a backdoor trigger. Our attack works wellacross datasets and models, even when the trigger presents in the physicalworld.  We explore the space of defenses and find that, surprisingly, our attack canevade the latest state-of-the-art defenses in their vanilla form, or after asimple twist, we can adapt to the downstream defenses. We study the cause ofthe intriguing effectiveness and find that because the trigger synthesized byour attack contains features as persistent as the original semantic features ofthe target class, any attempt to remove such triggers would inevitably hurt themodel accuracy first.

Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information