Bluetooth is a short-range wireless communication technology widely used by billions of personal computing, IoT, peripheral, and wearable devices. Bluetooth devices exchange commands and data, such as keyboard/mouse inputs, audio, and files, through a secure communication channel that is established through a pairing process. Due to the sensitivity of those commands and data, security mechanisms, such as encryption, authentication, and authorization, have been developed and adopted in the standards. Nevertheless, vulnerabilities continue to be discovered.
Previous adversarial audio attacks have mainly focused on ensuring the effectiveness of attacking an audio signal classifier via creating a small noise-like perturbation on the original signal. It is still unclear if an attacker is able to create audio signal perturbations that can be well perceived by human beings in addition to its attack effectiveness. In this work, we formulate the adversarial attack against music signals as a new perception-aware attack framework, which integrates human study into adversarial attack design. Specifically, we invite human participants to rate their perceived deviation based on pairs of original and perturbed music signals, and reverse-engineer the human perception process by regression analysis to predict the human-perceived deviation given a perturbed signal. The perception-aware attack is then formulated as an optimization problem that finds an optimal perturbation signal to minimize the prediction of perceived deviation from the regressed human perception model. Experiments show that the attack produces adversarial music with significantly better perceptual quality than prior work against YouTube's copyright detector.
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In this paper, we propose SpecPatch, a human-in-the loop adversarial audio attack on automated speech recognition (ASR) systems. Existing audio adversarial attacker assumes that the users cannot notice the adversarial audios, and hence allows the successful delivery of the crafted adversarial examples or perturbations. However, in a practical attack scenario, the users of intelligent voice-controlled systems (e.g., smartwatches, smart speakers, smartphones) have constant vigilance for suspicious voice, especially when they are delivering their voice commands. Once the user is alerted by a suspicious audio, they intend to correct the falsely-recognized commands by interrupting the adversarial audios and giving more powerful voice commands to overshadow the malicious voice. This makes the existing attacks ineffective in the typical scenario when the user's interaction and the delivery of adversarial audio coincide. To truly enable the imperceptible and robust adversarial attack and handle the possible arrival of user interruption, we design SpecPatch, a practical voice attack that uses a sub-second audio patch signal to deliver an attack command and utilize periodical noises to break down the communication between the user and ASR systems. We analyze the CTC (Connectionist Temporal Classification) loss forwarding and backwarding process and exploit the weakness of CTC to achieve our attack goal. Compared with the existing attacks, we extend the attack impact length (i.e., the length of attack target command) by 287%. Furthermore, we show that our attack achieves 100% success rate in both over-the-line and over-the-air scenarios amid user intervention.
The use of smartphones as voice recorders has made it easy to record audios as proof of conversations, but sharing of such audio evidence incurs speech and voice privacy risks. However, protecting speech/voice privacy without losing audio authenticity is challenging. The conventional post-process redaction and voice conversion of audio recordings, which invalidate their original signatures, make the audio unverifiable and prone to tampering. In this paper, we present Overo, an audio recording/sharing solution that supports privacy processing without losing audio authenticity. Overo records a realtime audio stream in the standard AAC-encoded format and allows privacy post-processing prior to sharing of audios while keeping their original signatures valid (even after the post redaction and voice conversion), guaranteeing no tampering since the time of their recording. Therefore, users can post-process their recordings to desired levels of privacy on speech (what content to redact) and speakers (whose voice to disguise) at the time of audio release, and still prove their authenticity. Overo is readily implementable in today's commodity smartphones. Our prototype on iPhones/Android phones demonstrates the production of AAC-compliant, tamperproof, and self-authenticating audios with speech/voice privacy protected based on users' post-recording decisions. 2ff7e9595c
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