Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University), Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Model inversion reverse-engineers input samples from a given model, and hence poses serious threats to information confidentiality. We propose a novel inversion technique based on StyleGAN, whose generator has a special architecture that forces the decomposition of an input to styles of various granularities such that the model can learn them separately in training. During sample generation, the generator transforms a latent value to parameters controlling these styles to compose a sample. In our inversion, given a target label of some subject model to invert (e.g., a private face based identity recognition model), our technique leverages a StyleGAN trained on public data from the same domain (e.g., a public human face dataset), uses gradient descent or genetic search algorithm, together with distribution based clipping, to find a proper parameterization of the styles such that the generated sample is correctly classified to the target label (by the subject model) and recognized by humans. The results show that our inverted samples have high fidelity, substantially better than those by existing state-of-the-art techniques.

View More Papers

NSFuzz: Towards Efficient and State-Aware Network Service Fuzzing

Shisong Qin (Tsinghua University), Fan Hu (State Key Laboratory of Mathematical Engineering and Advanced Computing), Bodong Zhao (Tsinghua University), Tingting Yin (Tsinghua University), Chao Zhang (Tsinghua University)

Read More

FANDEMIC: Firmware Attack Construction and Deployment on Power Management...

Ryan Tsang (University of California, Davis), Doreen Joseph (University of California, Davis), Qiushi Wu (University of California, Davis), Soheil Salehi (University of California, Davis), Nadir Carreon (University of Arizona), Prasant Mohapatra (University of California, Davis), Houman Homayoun (University of California, Davis)

Read More

Progressive Scrutiny: Incremental Detection of UBI bugs in the...

Yizhuo Zhai (University of California, Riverside), Yu Hao (University of California, Riverside), Zheng Zhang (University of California, Riverside), Weiteng Chen (University of California, Riverside), Guoren Li (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Manu Sridharan (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside),…

Read More

Testability Tarpits: the Impact of Code Patterns on the...

Feras Al Kassar (SAP Security Research), Giulia Clerici (SAP Security Research), Luca Compagna (SAP Security Research), Davide Balzarotti (EURECOM), Fabian Yamaguchi (ShiftLeft Inc)

Read More