The advent of voice cloning has fundamentally threatened the role of voice as a unique biometric. Many criminal incidents have already been reported to demonstrate its significant risks of identity forgery. Previous works explored the risks of voice cloning in constrained settings, which require victim speakers to either be already seen in the training data of voice cloning models, or leak dozens of minutes of their speech samples to adversaries. However, with the rapid progress of voice cloning in AIGC (Artificial Intelligence Generated Content) era, these requirements have largely been released, leaving the exact risks of state-of-the-art (SOTA) voice cloning techniques shrouded in a dense fog. To uncover it, this paper conducts a large-scale study in real-world scenarios to assess the risks of advanced voice cloning techniques. This study involves 5 SOTA voice cloning techniques (open-source and commercial), across 8 SOTA voice authentication systems (open-source and real-world) and 30 human listeners, using voice data of over 7,000 speakers (public and custom). By experimental and theoretical analysis, this study reveals that 1) state-of-the-art voice cloning techniques pose severe threats in spoofing voice authentication systems and human listeners; 2) demographic factors such as age and gender of victim speakers have a subtle impact on voice cloning attacks; 3) human listeners' subjective opinions and background about voice cloning play an important role in their susceptibility to attacks; 4) advanced detection methods still fail to identify voice cloning samples as expected.

The detection and recognition of Traffic Signs (TSs) by smart vehicles is of great interest. In this paper, we present a novel TSs localisation and recognition system using client-server architecture. To the best of our knowledge, this is the first architecture that uses non vision-based techniques to detect and recognise TSs. To discover TSs in lanes using our architecture, each travelling vehicle (known as client in our architecture) begins by sending a request, which contains its geographical position, to the data server. This server then sends all nearby TSs, found within a given radius in the lane, to the requester. Finally, we apply a filtering algorithm to select appropriate TSs from the previously found set of TSs. Our system can be used in a conjunction with a traditional vision-based system to further increase the accuracy of the whole system.