Can DeepFake Speech be Reliably Detected?

Abstract: Recent advances in text-to-speech (TTS) systems, particularly those with voice cloning capabilities, have made voice impersonation readily accessible, raising ethical and legal concerns due to potential misuse for malicious activities like misinformation campaigns and fraud. While synthetic speech detectors (SSDs) exist to combat this, they are vulnerable to ``test domain shift", exhibiting decreased performance when audio is altered through transcoding, playback, or background noise. This vulnerability is further exacerbated by deliberate manipulation of synthetic speech aimed at deceiving detectors. This work presents the first systematic study of such active malicious attacks against state-of-the-art open-source SSDs. White-box attacks, black-box attacks, and their transferability are studied from both attack effectiveness and stealthiness, using both hardcoded metrics and human ratings. The results highlight the urgent need for more robust detection methods in the face of evolving adversarial threats.

• The paper presents a comprehensive evaluation of DeepFake detection vulnerabilities against white-box, black-box, and agnostic adversarial attacks.
• Key findings include nearly complete evasion with white-box methods and significant transferability of attacks between different detection models.
• The study underscores the need for robust training data and complementary security measures to enhance synthetic speech detectors.

Evaluation of DeepFake Speech Detection

The paper explores the critical challenge of detecting DeepFake speech, a growing concern as text-to-speech (TTS) systems with voice cloning capabilities advance. These advances pose significant ethical and legal issues, particularly when synthetic voice is misused for fraudulent or malicious purposes. Synthetic speech detectors (SSDs), while designed to combat such misuse, demonstrate vulnerabilities, especially when faced with domain shifts or active adversarial attacks. This study presents the first comprehensive examination of these vulnerabilities, providing insights into attack strategies and their effectiveness against state-of-the-art SSDs.

Adversarial Attack Framework

The paper explores various adversarial attack models, focusing on three primary scenarios: white-box, black-box, and agnostic attacks. Each scenario reflects a different level of access the attacker might have to the detection system, ranging from complete access (white-box) to no access (agnostic). The study evaluates the effectiveness of these attacks in bypassing SSDs, utilizing both objective metrics and human ratings to assess the quality and stealthiness of the manipulated audio.

Key Findings

The findings reveal several critical insights:

• Vulnerability and Access: Increased access to the SSD model significantly enhances attack success rates. White-box attacks, using Projected Gradient Descent (PGD) and Iterative Fast Gradient Sign Method (I-FGSM), demonstrate nearly complete success in evading detection, particularly when targeting SSDs with synthetic audio generated by unfamiliar TTS systems.
• Transferability: The study highlights the potential for adversarial examples to transfer between different models. This transferability suggests that attacks crafted on one model can effectively bypass other models, emphasizing a broader vulnerability across various SSDs.
• Practical and Theoretical Implications: The paper underscores the inadequacy of current open-source SSDs against adversarial threats. This inadequacy is particularly pronounced when detectors encounter perturbed or out-of-domain synthetic audio. The research points towards the necessity for more robust and adaptive SSD systems, capable of handling unseen and evolving threats.

Implications for Future Research and Development

The paper’s findings have significant implications for the future development of SSDs and TTS systems:

1. Robust Training Data: To enhance SSD robustness, training datasets must include a diverse range of TTS outputs and be routinely updated. Given the rapid evolution of TTS technologies, continuous data update strategies are critical.
2. Confidentiality in Data Composition: Maintaining the confidentiality of training data composition can mitigate targeted attacks that exploit known information about detector training data.
3. Complementary Approaches: The study advocates for integrating robust audio authenticity techniques alongside SSDs. Techniques such as robust watermarking may provide an additional layer of security, especially as TTS systems approach near-human quality.

Conclusion

The systematic study of adversarial attacks on open-source synthetic speech detectors reveals substantial vulnerabilities that necessitate improvement. The insights provided pave the way for developing more resilient SSD systems, emphasizing the importance of comprehensive data strategies and complementary security measures. The evolving landscape of synthetic voice technology presents ongoing challenges that researchers and practitioners must address through innovative, multi-faceted approaches in AI security.