Going In Style: Audio Backdoors Through Stylistic Transformations

Koffas, Stefanos, Pajola, Luca, Picek, Stjepan, Conti, Mauro

arXiv.org Artificial Intelligence 

This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects. We first formalize stylistic triggers - currently missing in the literature. Second, we explore how to develop stylistic triggers in the audio domain by proposing JingleBack. Our experiments confirm the effectiveness of the attack, achieving a 96% attack success rate. Our code is available in https://github.com/skoffas/going-in-style.

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