Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion

Understand

Deep speech classification has achieved tremendous success and greatly promoted the emergence of many real-world applications.

• However, backdoor attacks present a new security threat to it, particularly with untrustworthy third-party platforms, as pre-defined triggers set by the attacker can activate the backdoor.
• Most of the triggers in existing speech backdoor attacks are sample-agnostic, and even if the triggers are designed to be unnoticeable, they can still be audible.
• This work explores a backdoor attack that utilizes sample-specific triggers based on voice conversion.