Masahiro Kaneko · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28045 · open-access PDF
Um sich gegen Backdoor-Angriffe auf neuronale Netzwerke zu verteidigen, muss der Verteidiger die Verifizierungsfunktion identifizieren, die bestimmt, ob ein Eingang ein Trigger ist, anstatt einzelne Trigger isoliert abzulehnen, da ein Angreifer neue Trigger generieren kann, die den gleichen Verifizierer schneller befriedigen, als sie blockiert werden können.
Machine translation (Meta M2M100); the English original follows.
To defend against backdoor attacks on neural networks, the defender must identify the verifier function that determines whether an input is a trigger, rather than rejecting individual triggers in isolation, since an attacker can generate new triggers satisfying the same verifier faster than they can be blocked. If a backdoor verifier could be installed with the asymmetry of a cryptographic authentication scheme, in which only the attacker can produce trigger inputs while the defender cannot reverse-engineer the verifier from observations, learning-based defense would be impossible in principle
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