Voice Clones and Audio Deepfakes: The Security Threats Are Real

Generative AI has driven an unprecedented advancement of voice technologies, resulting in the ability to create hyper-realistic audio deepfakes and voice clones. With a short recording of someone’s voice, there are multiple tools available that can be used to create voice clones. They can be used to generate speech that is virtually indistinguishable from that of the original speaker. Paired with text-to-speech (TTS) technology, a cloned voice can speak whatever can be typed. By integrating with conversational AI technology, we can communicate with intelligent voicebots in real time as we do with humans. The benefits of voice cloning are just beginning to be recognized, from revolutionizing content creation with realistic voiceovers to providing personalized customer service experiences, to assisting individuals with speech impairments or language barriers.

The voice clone threat

As with many life-changing innovations, there is the potential for exploitation of voice clone technology by bad actors. Voice data collected without the knowledge or consent of a victim is a strong signal of an attempt at crime. Just as deepfake imagery and videos now compel us to ask the question: “Is this an image of something that really happened or a digital creation?”  With the advent of voice clones, we must also ask: “Did the person who this sounds like really say this? Are they actually speaking to me right now?”  

Voice clones have profound implications on public opinion and trust, particularly in an era dominated by digital communication. The security implications of voice cloning are extensive and varied. Here are a few of the hazards:

Misinformation. Bad actors can publish audio of statements purportedly made by public figures when in fact they were not really said. The goal is to spread misinformation and erode trust in real statements.  

Defamation. Voice clones can be used to attribute statements to people falsely, distributing content making them say things they didn’t say to cause reputational harm. 

Appropriation. While copyright and trademark laws are still catching up with the technology, voice clones threaten the careers of accomplished performers and voiceover artists where they can be used in their place. 

Vishing and extortion. Vishing is a form of phishing using voice calls and messages instead of email. Fraudsters can use voice clones to leave convincing messages or even speak in real time with a voice that sounds like a trusted party or someone known to their target. Fraudsters conduct vishing attacks to collect private information and access to their victims’ accounts. Kidnappings have been faked by creating voice clones of loved ones to demand a ransom. 

Biometric attacks. Voice clones pose a threat to voice-based biometric security. Without detection measures, speaker verification used for identity authentication can be easily deceived by clones, leading to identity theft, account takeover, and data breaches.

Deeper dive: replays, software and hardware clone attacks and their detection

A replay attack is when a fraudster attempts to spoof biometric authentication security by playing an audio recording of a voice to the microphone to impersonate their victim. This can also be done using a voice clone. For example, if voice-based biometric authentication requires a specific passphrase, a voice clone could be used to speak the correct passphrase in the voice of the account owner. Combining replay detection with clone detection will provide a robust countermeasure to this form of attack.

Hardware- and software-based clone attacks attempt to bypass replay detection by playing a clone player into a hijacked microphone, virtual microphone, or emulator in such a way that bypasses the primary microphone. In this case, there is no replay and the audio signal appears to be coming from a live voice. Replay detection will not detect this attack, but clone detection will.

Just as machine learning is used to create voice clones, it also can be used to train algorithms to detect them. Voice clone detection leverages AI to analyze various voice parameters, looking for inconsistencies and anomalies that indicate that voice cloning technology is evident. Machine learning algorithms can be trained using millions of audio files to discover artifacts in an audio signal that are not perceptible to the human ear but nevertheless indicate that the voice contains a clone.

The ability to detect covert voice clones will allow them to achieve their full potential

The advancement of voice cloning technology is intrinsically linked to the development of robust security measures and ethical guidelines to prevent their use for harm. As the technology continues to advance, so too must the detection techniques, with artificial intelligence playing a pivotal role in increasing detection accuracy, particularly in the telephony channel. The establishment of clear ethical guidelines governing the use of voice cloning is equally crucial, ensuring responsible use and mitigating potential harms.

Voice cloning technology represents a double-edged sword, offering unparalleled benefits while also introducing significant security risks. The journey ahead requires vigilance, innovation, and a commitment to ethical practices. By embracing advanced detection techniques, fostering user awareness, and establishing comprehensive legal frameworks, we can navigate the challenges posed by voice clones and unlock the full potential of this groundbreaking technology, ensuring a secure and trustworthy digital future.

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