Human Distinguishable Visual Key Fingerprints

Visual fingerprints are used in human verification of identities to improve security against impersonation attacks. The verification requires the user to confirm that the visual fingerprint image derived from the trusted source is the same as the one derived from the unknown source. We introduce CEAL, a novel mechanism to build generators for visual fingerprint representations of arbitrary public strings. CEAL stands out from existing approaches in three significant aspects: (1) eliminates the need for hand curated image generation rules by learning a generator model that imitates the style and domain of fingerprint images from a large collection of sample images, hence enabling easy customizability, (2) operates within limits of the visual discriminative ability of human perception, such that the learned fingerprint image generator avoids mapping distinct keys to images which are not distinguishable by humans, and (3) the resulting model deterministically generates realistic fingerprint images from an input vector, where the vector components are designated to control visual properties which are either readily perceptible to a human eye, or imperceptible, yet necessary for accurately modeling the target image domain.

Unlike existing visual fingerprint generators, CEAL factors in the limits of human perception, and pushes the key payload capacity of the images toward the limits of its generative model: We have built a generative network for nature landscape images which can reliably encode 123 bits of entropy in the fingerprint. We label 3,996 image pairs by 931 participants. In experiments with 402 million attack image pairs, we found that pre-image attacks performed by adversaries equipped with the human perception discriminators that we build, achieve a success rate against CEAL that is at most 0.0002%. The CEAL generator model is small (67MB) and efficient (2.3s to generate an image fingerprint on a laptop).