Image-Level Iris Morph Attack

Abstract

We investigate the problem of morph attacks in the context of iris biometrics. A morph attack entails the generation of an image that embodies two different identities. This is accomplished by combining, i.e., morphing, two biometric samples pertaining to two different identities. While such an attack is being increasingly studied in the context of face recognition, it has not been widely analyzed in iris recognition. In this work, we perform iris morphing at the image-level and generate morphed iris images using two available datasets (IITD and WVU multi-modal). We demonstrate the vulnerability of three different iris recognition methods to morph attacks with a success rate of over 90% at a false match rate of 0.01%. We also analyze the textural similarity required between the component images to create a successful morphed image. Finally, we provide preliminary results on the detection of morphed iris images.