Name
Abstract | ||
|---|---|---|
ASR (Automatic Speech Recognition) technology is a key technology for human-computer interaction. Especially the DNN models of wake-up-word speech recognition, which enables the smart device to recognize wake-up words spoken by users when they are in the sleep or lock screen state, allowing the device to directly enter the wait command state, and start the first step of voice interaction. ASR technology no wonder provides great convenience for people's daily life, but it's security problem has always been a hot topic of further research. The rapid development of ASR models has made these models very vulnerable which seriously affect their performance in real scenarios. This paper proposes a new backdoor attack method TNN (Trojan Neural Network) for deep learning models in voice wake-up scenarios. The attacker leaves backdoor in the deep learning model of the ASR. Besides specific wakeup words, attackers can also use other words to force the device to wake up and get the IoT smart device's highest privileges. And when the smart device is in the awake state, any audio containing a particular vocabulary will be recognized as a specific command and executed. In this papaer, we propose a novel method to attack ASR model, and without affecting the performance of clean samples, by applying the attack method, the success rate in compulsory recognition of specific words can be up to 100%. The experimental results prove that our attack method is very effective and poses a great security problem of the voice-interactive IoT device. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CSCWD49262.2021.9437669 | PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) |
Keywords | DocType | Citations |
Attack, ASR, DNN, TNN, Wake-Up | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxuan Li | 1 | 1 | 2.37 |
| Xiao Wang | 2 | 92 | 9.26 |
| Dongdong Huo | 3 | 3 | 2.75 |
| Han Wang | 4 | 0 | 0.34 |
| Chao Liu | 5 | 25 | 10.08 |
| Yazhe Wang | 6 | 3 | 1.07 |
| Yu Wang | 7 | 2 | 1.71 |
| Zhen Xu | 8 | 21 | 17.33 |