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It is critical to understand the privacy and robustness vulnerabilities ofmachine learning models, as their implementation expands in scope. Inmembership inference attacks, adversaries can determine whether a particularset of data was used in training, putting the privacy of the data at risk.Existing work has mostly focused on image related tasks; we generalize thistype of attack to speaker identification on audio samples. We demonstrateattack precision of 85.9\% and recall of 90.8\% for LibriSpeech, and 78.3\%precision and 90.7\% recall for VOiCES (Voices Obscured in ComplexEnvironmental Settings). We find that implementing defenses such as predictionobfuscation, defensive distillation or adversarial training, can reduce attackaccuracy to chance.

Reducing audio membership inference attack accuracy to chance: 4 defenses