EMIRIS: Eavesdropping on Iris Information via Electromagnetic Side Channel

—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.