Wenhao Li (Shandong University), Jiahao Wang (Shandong University), Guoming Zhang (Shandong University), Yanni Yang (Shandong University), Riccardo Spolaor (Shandong University), Xiuzhen Cheng (Shandong University), Pengfei Hu (Shandong University)

Iris recognition is one of the most secure biometric methods due to the uniqueness and stability of iris patterns, as well as their resistance to forgery. Consequently, it is frequently used in high-security authentication scenarios. However, systems using Near-Infrared (NIR) sensors may expose the iris information of users, leading to significant privacy risks. Our research found that the electromagnetic (EM) emissions generated during data transmission of NIR sensors are closely related to iris data. Based on this observation, we propose EMIRIS, a method for reconstructing the iris information using EM side channels. By deconstructing the digital signal transmission format of the NIR sensors and the mapping mechanism of the iris data matrix, we can reconstruct iris information from EM signals and convert it into iris images. To improve the quality of the reconstructed iris, we model the denoising and restoration of iris texture details as a linear inverse problem and tailor a diffusion model to solve it. Extensive experimental evaluations show that EMIRIS can effectively reconstruct iris information from commercial iris recognition devices, achieving an average SSIM of 0.511 and an average FID of 7.25. Even more concerning, these reconstructed irises can effectively spoof the classical iris recognition model with an average success rate of 53.47% on more than 3,000 iris samples from 50 different users.

View More Papers

BitShield: Defending Against Bit-Flip Attacks on DNN Executables

Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

Read More

Query Privacy in Data Spaces

Shuwen Liu (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China), George C. Polyzos (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China and ExcID P.C., Athens, Greece)

Read More

AI-Assisted RF Fingerprinting for Identification of User Devices in...

Aishwarya Jawne (Center for Connected Autonomy & AI, Florida Atlantic University), Georgios Sklivanitis (Center for Connected Autonomy & AI, Florida Atlantic University), Dimitris A. Pados (Center for Connected Autonomy & AI, Florida Atlantic University), Elizabeth Serena Bentley (Air Force Research Laboratory)

Read More

Onion Franking: Abuse Reports for Mix-Based Private Messaging

Matthew Gregoire (University of North Carolina at Chapel Hill), Margaret Pierce (University of North Carolina at Chapel Hill), Saba Eskandarian (University of North Carolina at Chapel Hill)

Read More