Similarity (geometry)

Deep learning

Image (mathematics)

Machine learning

Audio signal processing

Engineering

Speech coding

Artificial intelligence

Systems engineering

Task (project management)

Speech recognition

Deep neural networks

Adversarial system

Computer science

Artificial neural network

Audio signal

Black box

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity, with 35% targeted attack success rate, after 3000 generations while maintaining 94.6% audio file similarity.

Targeted Adversarial Examples for Black Box Audio Systems