Abstract
Nowadays, deep learning models have many applications in social life. Specifically, the generative adversarial network (GAN) has many applications such as multimodal image-to-image translation, text to image, image filter, image editing, stylized images, data augmentation. However, deep neural networks are vulnerable to inference attacks as they memorize information about their training data. In this study, we set up black-box and white-box attacks to comprehensively evaluate the privacy of generalization models on the LFW dataset and CIFAR dataset. In addition, we measured the leakage of private information through the parameters of the fully trained model as well as the parameter updates of the model during training. In a white box attack setup, we evaluated inference attacks against GAN by monitoring their training data samples. In the black box attack setup, we divided it into two types of black box attacks with supporting information and without supporting information. We assumed that the attacker had about 10% to 20% of the target model training dataset in the black box attack with supporting information. Finally, we concluded the relationship between the number of training epochs and the GAN properties with information leakage.
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Acknowledgment
This work is supported by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 42/2019/HD-QPTKHCN, dated 11/7/2019). We also thank all members of AC Lab and D-STAR Lab for their great supports and comments during the preparation of this paper.
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Ha, T., Dang, T.K., Nguyen-Tan, N. (2021). Comprehensive Analysis of Privacy in Black-Box and White-Box Inference Attacks Against Generative Adversarial Network. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_21
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