Evaluating the robustness of imperceptible adversarial samples in real-world scenarios, such as over the air and through telephony networks, presents several challenges. One significant challenge is the potential degradation of audio quality during transmission. Telephony networks often employ compression algorithms that can alter the characteristics of audio signals, potentially masking or even eliminating the crafted perturbations intended to deceive detection systems. This could lead to a situation where adversarial samples that are effective in controlled environments fail to maintain their imperceptibility and effectiveness in real-world applications.
Another challenge is the variability in environmental factors that can affect audio transmission, such as background noise, echo, and interference. These factors can introduce additional distortions that may either enhance or diminish the effectiveness of adversarial samples. For instance, background noise could obscure the subtle perturbations added to the audio, making it more difficult for detection systems to identify them, but it could also interfere with the clarity of the synthetic speech, potentially raising suspicion.
Moreover, the dynamic nature of voice conversion technologies and the rapid advancements in deepfake detection systems necessitate continuous evaluation and adaptation of adversarial strategies. As detection models evolve, adversarial samples that were once effective may become easily detectable, requiring ongoing research and development to ensure that the methods remain robust against emerging threats.