Going in Style: Audio Backdoors Through Stylistic Transformations-Reference-Cited by-同舟云学术

Going in Style: Audio Backdoors Through Stylistic Transformations

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Koffas Stefanos¹,Pajola Luca²,Picek Stjepan³,Conti Mauro²

Affiliation:

1. Delft University of Technology,Cybersecurity Group,The Netherlands

2. University of Padua,Security and Privacy Research Group,Italy

3. Radboud University,Digital Security Group,The Netherlands

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096332.pdf?arnumber=10096332

Reference23 articles.

1. Large image datasets: A pyrrhic win for computer vision?;prabhu;CoRR,2020

2. Common voice: A massively-multilingual speech corpus;ardila,2019

3. Speech database development at MIT: Timit and beyond

4. Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness;geirhos;International Conference on Learning Representations,2019

5. Can You Hear It?

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1. Audio Steganography Based Backdoor Attack for Speech Recognition Software;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

2. FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Breaking Speaker Recognition with Paddingback;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Toward Stealthy Backdoor Attacks Against Speech Recognition via Elements of Sound;IEEE Transactions on Information Forensics and Security;2024

5. Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions;IEEE Access;2024