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In this paper, we demonstrated the existence of the universal adversarial perturbations~(UAPs) for the speaker recognition systems. We proposed a generative network to learn the mapping from the low-dimensional normal distribution to the UAPs subspace, then synthesize the UAPs to perturbe any input signals to spoof the well-trained speaker recognition model with high probability. Experimental results on TIMIT and LibriSpeech datasets demonstrate the effectiveness of our model.
If you use this code or part of it, please cite us!
Jiguo Li, Xinfeng Zhang, Chuanming Jia, Jizheng Xu, Li Zhang, Yue Wang, Siwei Ma, Wen Gao, "universal adversarial perturbations generative network for speaker recognition" arxiv, IEEE Xplore.
prepare the data, split the audio data into slices:
python ./common/prepare_dataset.py --type speaker --data_root TIMIT_DATA_ROOT --output_dir TIMIT_DATA_ROOT/processed
In the project root directory, run
python ./train_generator.py --output_dir ./output/timit_generator --no_dist --noise_scale 1 --norm_factor 1000 --norm_clip 0.01 --speaker_factor 1 --wlen 200 --frame_dim 3200 --margin 10 --data_root DATAROOT --dataset timit --mul 0.1 --epoch 50
An example of the script:
python ./universal_attacker/train_generator.py --output_dir ./output/generator/timit_generator_norm2000_mul01_wlen800_margin10_epoch50_clip001_nolrdecay_fixdmse --speaker_model ./output/SincNet_TIMIT/model_raw.pkl --speaker_cfg ./config/timit_speaker_generator.cfg --noise_scale 1 --norm_factor 2000 --mul 0.1 --norm_clip 0.01 --speaker_factor 1 --wlen 800 --frame_dim 12800 --margin 10 --epoch 50 --data_root /data00/jiguo/dataset/TIMIT_lower --dataset timit --num_workers 8 --test_output ./output/generator/timit_generator_norm2000_mul01_wlen800_margin10_epoch50_clip001_nolrdecay_fixdmse_test --no_dist
The training for TIMIT dataset will cost about 2 day on a 1080ti GPU.
More results can be found on our UAPs for speaker recognition project main page.
Thanks to the valuable discussion with Jing Lin. Besides, thanks to the open source of SincNet, Pytorch-Kaldi, PESQ.
Please feel free to contact me (jiguo.li@vipl.ict.ac.cn, jgli@pku.edu.cn) if you have any questions about this project. Note that this work is only for research. Please do not use it for illegal purposes.