Publications

Guangke Chen, Zhe Zhao, Fu Song, Sen Chen, Lingling Fan, Feng Wang, Jiashui Wang

'Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition'

IEEE Transactions on Dependable and Secure Computing (TDSC), CCF-A, IF=6.791

[paper] [code] [website]

Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Taolue Chen

'Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach'

ACM Transactions on Software Engineering and Methodology (TOSEM, CCF-A)

Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Min Zhang, Taulue Chen, Jun Sun

'QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks'

ASE 2022, CCF-A

Zhe Zhao, Yedi Zhang, Guangke Chen, Fu Song, Taolue Chen and Jiaxiang Liu

'ACROBAT: Accelerating CEGAR-based Neural Network Verification via Adversarial Attacks'

SAS 2022, CCF-B

Guangke Chen, Zhe Zhao, Fu Song, Sen Chen, Lingling Fan, Yang Liu

'AS2T: Arbitrary source-to-target adversarial attack on speaker recognition systems'

IEEE Transactions on Dependable and Secure Computing (TDSC), CCF-A, IF=6.791

[paper] [blog]

Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

'Turn Lemons into Lemonade: Using Adversarial Attack Methods to Detect Abnormal Examples'

Under major revision of TDSC

Guangke Chen, Zhe Zhao, Fu Song, Sen Chen, Lingling Fan, Yang Liu.

'SEC4SR: A Security Analysis Platform for Speaker Recognition'.

Preprint

[paper] [code] [website]

Guangke Chen, Sen Chen, Lingling Fan, Xiaoning Du, Zhe Zhao, Fu Song, Yang Liu.

'Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems'.

Oakland 2021, CCF-A

Acceptance Rate: 115/952=12%

[paper] [website] [code] [slide] [talk] [blog] [press-1] [press-2] [press-3]

Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun.

'Attack as Defense: Characterizing Adversarial Examples using Robustness'.

ISSTA 2021, CCF-A

Acceptance Rate: 51/219=23%

[paper] [code]

Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Taolue Chen.

'BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks'.

CAV 2021, CCF-A

Acceptance Rate: 79/290=27%

[paper]