Screen Perturbation

Screen Perturbation: Adversarial Attack and Defense on Under-Screen Camera

Ye, Hanting (TU Delft Embedded Systems)
Lan, G. (TU Delft Embedded Systems)
Jia, Jinyuan (The Pennsylvania State University)
Wang, Q. (TU Delft Embedded Systems)

2023

Smartphones are moving towards the fullscreen design for better user experience. This trend forces front cameras to be placed under screen, leading to Under-Screen Cameras (USC). Accordingly, a small area of the screen is made translucent to allow light to reach the USC. In this paper, we utilize the translucent screen's features to inconspicuously modify its pixels, imperceptible to human eyes but inducing perturbations on USC images. These screen perturbations affect deep learning models in image classification and face recognition. They can be employed to protect user privacy, or disrupt the front camera's functionality in the malicious case. We design two methods, one-pixel perturbation and multiple-pixel perturbation, that can add screen perturbations to images captured by USC and successfully fool various deep learning models. Our evaluations, with three commercial full-screen smartphones on testbed datasets and synthesized datasets, show that screen perturbations significantly decrease the average image classification accuracy, dropping from 85% to only 14% for one-pixel perturbation and 5.5% for multiple-pixel perturbation. For face recognition, the average accuracy drops from 91% to merely 1.8% and 0.25%, respectively.

Under-screen camera
adversarial perturbation
privacy

http://resolver.tudelft.nl/uuid:d2d0fa44-c7be-48ca-a777-2e6754acc25e

https://doi.org/10.1145/3570361.3613278

Association for Computing Machinery (ACM), New York

978-1-4503-9990-6

ACM MobiCom '23: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking

ACM MobiCom '23, 2023-10-02 → 2023-10-06, Madrid, Spain

Institutional Repository

book chapter

© 2023 Hanting Ye, G. Lan, Jinyuan Jia, Q. Wang