Facial photo filters are widely used when sharing selfies, but how others perceive filtered faces has rarely been examined experimentally. In this online experiment, I examined whether a typical facial photo filter from Instagram affects judgments of the attractiveness and trustworthiness of female faces. To rule out familiarity effects, 40 AI-generated (StyleGAN) faces of women (39 analyzed) were presented either unfiltered or modified with a filter that combines soft-focus, contrast, and exposure adjustments. Participants (N = 69, 39 women and 30 men, aged 18–63 years) rated each face on 10-point scales. The four questionnaire versions were randomly allocated to recruitment channels rather than to individual participants, so condition was confounded with viewer gender (filter: 28 men and 6 women, no filter: 33 women and 2 men). Filtered faces received slightly lower mean ratings of attractiveness (Hedges’ g = −0.32, 95% CI [−0.79, 0.15]) and trustworthiness (g = −0.32, 95% CI [−0.79, 0.15]), but neither difference was statistically significant. Mixed-effects models with crossed random effects for participants and faces led to the same conclusion. Adjusting for viewer gender reduced the attractiveness difference to nearly zero but changed the trustworthiness difference only slightly. Gender differences could not be tested. About half of the women and a quarter of the men reported editing photos of themselves. The extent of editing was not significantly correlated with age or education. The findings do not support the assumption that such filters lower these judgments. Adequately powered studies with randomization at the participant level are needed.