Generative AI, a technology with endless possibilities, has recently seen the development of advanced versions called WormGPT and DarkBART. These AI systems have raised concerns due to their potential for exploitation. One particular area of concern is the rise of video and voice deepfakes, which are hyper-realistic and make it difficult to distinguish between genuine and fabricated content. Voice deepfakes, in particular, pose a significant threat as they can convincingly mimic the speech patterns and tone of the person being portrayed.
The use of deepfakes is not a new concept, but advancements in generative AI have made it increasingly challenging to detect their misuse. This calls for urgent solutions to detect and prevent the spread of voice deepfakes, as they can cause substantial financial losses, damage reputations, and propagate misinformation on a large scale.
So, how can we differentiate between fake and genuine content? Detecting voice deepfakes requires careful attention to details and staying informed about cybersecurity developments. According to a report by cybersecurity company Kaspersky, there are several clues to identify voice deepfakes. Fraudulent calls with threats or requests may have poor audio quality or distracting background noise. Monotony in speech and confusion in the pronunciation of certain words can also be indicators. Voice deepfake messages that sound urgent and require immediate action, such as sharing passwords or account details, should be verified with the concerned person before taking any action.
An example of a voice deepfake scam occurred in 2021 when a criminal posed as the CEO of a German energy firm and called the UK-based CEO, requesting a money transfer. The criminal used a deepfake voice with a German accent to convince the UK CEO. The transfer was made, resulting in financial losses. The UK CEO became suspicious when a second call requesting another transfer was made from an Austrian number.
To combat voice deepfakes, detection tools are being developed to sense fraudulent audio. These tools aim to identify deepfake voices in speeches by politicians or statements made by celebrities accusing others of fraud. However, achieving perfection in voice deepfake detection is still a challenge. Characteristics such as timbre, manner, and intonation of speech are considered to determine whether an audio piece is genuine or fake.
Dmitry Anikin, a Senior Data Scientist at Kaspersky, emphasized the importance of not making decisions based on emotions or a sense of urgency in response to voice deepfake messages. It is crucial to verify the authenticity of the caller before sharing any personal or financial information. Ignoring a voice deepfake message may not directly impact security, but it is always better to err on the side of caution and wait for further verification before taking any action.
In conclusion, the development of generative AI has brought about both exciting possibilities and concerning exploitations. Voice deepfakes have become a significant threat, and it is crucial to have effective detection solutions in place to protect individuals, organizations, and society at large. By paying close attention to details, staying informed about cybersecurity developments, and verifying the authenticity of messages, we can take steps to mitigate the risks associated with voice deepfakes.