Deepfakes pose a serious threat to cybersecurity. Companies are scrambling to protect their customers’ data against such emerging threats. Deepfake detection has become of paramount importance, especially in industries where customer authentication is necessary, such as contact centers.
Given the inherent risks related to identity verification in contact centers, it’s become quite important for organizations to protect themselves by using audio deepfake detection techniques.
What is Audio Deepfake Detection?
You’re probably already familiar with what a deepfake is — the use of a person’s likeness to replicate or manipulate a video to make it appear as if that person is saying or doing something they never actually did.
However, there’s an equally concerning counterpart in the audio realm. Audio deepfakes are fraudulent audio clips where voices of real individuals are either cloned or manipulated, making it sound as though they said things they never actually did.
The technology behind audio deepfakes relies on sophisticated machine learning algorithms, particularly deep learning models like Generative Adversarial Networks (GANs).
Using a sufficient amount of audio data from the targeted individual, these models can be trained to generate new audio that mimics the person’s unique vocal characteristics, intonations, and speech patterns.
Once generated, these deepfakes can be incredibly convincing, posing risks in various domains like politics, finance, and personal security.
In fact, there’s a very real risk that someone could create an audio deepfake of an individual’s voice, and use that to gain access to your personal accounts. For contact centers that don’t have a sound strategy against deepfakes, this could be a serious problem.
As you can imagine, interest in audio deepfake detection continues to rise. At Pindrop, we understand the concerns of our clients, and offer deepfake detection as part of our Passport and Protect products.
How Audio Deepfake Detection Works
At its core, Pindrop’s technology is based on deep voice biometrics and acoustic anomaly detection. It functions by analyzing various characteristics of an audio clip, beyond just the voice itself.
This means that while the voice might be expertly cloned or manipulated using deep learning models, there might still be subtle discrepancies or anomalies in the audio that give away its inauthenticity.
One primary method that Pindrop employs is to analyze the audio’s metadata and background noise.
Genuine audio recordings often have a certain ambient noise pattern, while deepfakes might show inconsistencies because they’re either generated in a noiseless environment or the background noise is artificially added later. By meticulously examining these patterns, Pindrop can spot potential manipulations.
Furthermore, Pindrop’s software doesn’t just look at the current state of audio deepfake technology; it anticipates future advancements.
By using machine learning and constantly updating its models based on new deepfake techniques and samples, it aims to stay ahead in the game.
This iterative approach ensures that the software is not just reactive but also proactive against the evolving deepfake threat landscape.
More importantly, Pindrop has a rapidly growing database of voice interactions, which play an important role in refining the voice models and ensures a high level of accuracy in detecting deepfakes.
Understanding the Technology behind Audio Deepfake Detection
There are a variety of technological aspects that are used to detect deepfakes in audio signals. Advanced deepfake detection software analyzes the spectral content of an audio signal, reviewing the inconsistencies that may indicate any signs of manipulation.
Going a layer deeper, they also evaluate features such as the kurtosis and the skewness of the audio signal, thus determining if the audio clip deviates from what you’d expect to be natural speech patterns.
More importantly, deepfakes may struggle to perfectly replicate minute acoustic features of human speech, such as formant frequencies, pitch, and tone variations. Detection software can be used to look for such anomalies and identify them.
Arguably the most effective way to identify audio deepfakes is the use of deep learning models that leverage machine learning and advanced neural networks such as Generative Adversarial Networks (GANs). These models are actively being trained, and are able to identify subtle nuances in recordings.
Genuine audio recordings often have consistent ambient noise patterns, as you know. Deepfakes often show anomalies in this regard, either due to being generated in a noiseless environment or because of artificially added noise post-generation. Detection tools can be used to analyze this and determine if the voice is real or a fake.
Audio Deepfakes – How They Affect Banking Transactions
Banks and financial institutions rely primarily on trust-based systems, and audio deepfakes have the potential to seriously undermine those. From fraudulent bank transfers to phishing attempts, audio deepfakes pose a considerable risk.
Malicious actors, armed with a near-perfect imitation of a bank customer’s voice, can deceive employees into transferring funds or authorizing transactions without the genuine customer’s knowledge.
The repercussions don’t end there. To counteract these threats, banks might need to invest heavily in advanced detection systems and employee training.
This increase in operational costs could indirectly affect customers through higher banking fees or changes in services.
With many banks incorporating voice biometrics into their multi-factor authentication processes, audio deepfakes can compromise these security measures, necessitating further investments in robust detection and prevention mechanisms.
Organizations that don’t use proper IVR/IVA authentication methods are often at an increased risk of being targeted, making it incredibly important for companies to shore up their defenses and make sure they protect themselves.
Combining Voice and Face Authentication to Improve Detection
Contact centers are now using technology to combine voice and face authentication, in a bid to improve overall security and ensure identities are verified as quickly as possible.
With Pindrop’s voice authentication solution, companies can prevent spoofing at every touchpoint. The voice biometrics system runs through the lifecycle of the call, analyzing and authenticating repeat callers. It becomes increasingly more accurate and efficient, ensuring that calls are authenticated in a very short time.
More importantly, this isn’t just limited to use in contact centers. Companies can use Pindrop’s solutions in smart devices as well, boosting overall privacy and ensuring only authorized users are given access to sensitive information.
