Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Perceptual and computational detection of face morphing
AU - Nightingale, Sophie J
AU - Agarwal, Shruti
AU - Farid, Hany
PY - 2021/3/31
Y1 - 2021/3/31
N2 - A relatively new type of identity theft uses morphed facial images in identification documents in which images of two individuals are digitally blended to create an image that maintains a likeness to each of the original identities. We created a set of high-quality digital morphs from passport-style photos for a diverse set of people across gender, race, and age. We then examine people’s ability to detect facial morphing both in terms of determining if two side-by-side faces are of the same individual or not and in terms of identifying if a face is the result of digital morphing. We show that human participants struggle at both tasks. Even modern machine-learning-based facial recognition struggles to distinguish between an individual and their morphed version. We conclude with a hopeful note, describing a computational technique that holds some promise in recognizing that one facial image is a morphed version of another.
AB - A relatively new type of identity theft uses morphed facial images in identification documents in which images of two individuals are digitally blended to create an image that maintains a likeness to each of the original identities. We created a set of high-quality digital morphs from passport-style photos for a diverse set of people across gender, race, and age. We then examine people’s ability to detect facial morphing both in terms of determining if two side-by-side faces are of the same individual or not and in terms of identifying if a face is the result of digital morphing. We show that human participants struggle at both tasks. Even modern machine-learning-based facial recognition struggles to distinguish between an individual and their morphed version. We conclude with a hopeful note, describing a computational technique that holds some promise in recognizing that one facial image is a morphed version of another.
U2 - 10.1167/jov.21.3.4
DO - 10.1167/jov.21.3.4
M3 - Journal article
VL - 21
SP - 1
EP - 18
JO - Journal of Vision
JF - Journal of Vision
SN - 1534-7362
IS - 3
M1 - 4
ER -