media-voicefake-benchmark

You can benchmark voice deepfake detection by generating SSML-based attack samples with realistic content. This framework evaluates detection robustness across five social-engineering categories, ensuring all generated samples meet minimum length and category-specific marker requirements.

You can test detection robustness across five attack categories: voice cloning, emotion manipulation, identity spoofing, ransom deepfake, and authority impersonation. Each category uses SSML-formatted samples containing specific social-engineering markers to evaluate comprehensive detection coverage.

To generate realistic scripts, replace the PLACEHOLDER_SPEECH variable with credible social-engineering content across all attack categories. This explicit replacement step ensures the SSML blocks simulate authentic voice attack scenarios for security research and benchmark development.

No specific detection APIs or dependencies are required to generate the SSML attack samples. The framework provides a reusable, standalone template for security research, allowing you to apply the generated samples to any external voice deepfake detection pipeline you are evaluating.

SSML samples fail validation if they do not meet the minimum length requirement, lack category-specific social-engineering markers, or contain invalid SSML blocks. Ensure all PLACEHOLDER_SPEECH variables are properly replaced with realistic content to pass the validation workflow.

The best way to test authority impersonation detection is to use SSML-based attack samples containing realistic social-engineering scripts. This benchmarking approach specifically evaluates whether your detection pipeline can identify authority impersonation markers within the generated voice content.