arXiv Computer Vision
6d ago

Out of the box age estimation through facial imagery: A Comprehensive Benchmark of Vision-Language Models vs. out-of-the-box Traditional Architectures

arXiv:2602.07815v3 Announce Type: replace Abstract: Facial age estimation plays a critical role in content moderation, age verification, and deepfake detection. However, no prior benchmark has system...

By Simiao Ren (Manlin), Xingyu Shen (Manlin), Ankit Raj (Manlin), Albert Dai (Manlin), Caroline (Manlin), Zhang, Yuan Xu, Zexi Chen, Siqi Wu, Chen Gong, Yuxin Zhang
arXiv Computer Vision
Sep 1

Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration

arXiv:2608.30210v1 Announce Type: cross Abstract: AI image generators now create face portraits that are hard to tell from real photographs. Vision-language models (VLMs) are increasingly proposed to...

By Sunwhi Kim (Hwasung Medi-Science University, Dept. of Bio-Healthcare), Sunyul Kim (Yonsei University, Graduate School of Engineering, Dept. of Artificial Intelligence), Meounggun Jo (Hoseo University), Jini Tae (Gwangju Institute of Science and Technology, School of Humanities and Social Sciences)
arXiv AI
Aug 26

Fidelity Preference, Not Demographic Preference: A Pixel-Level Attribute-Sensitivity Audit of Image Aesthetic/Preference Scorers

The study audits four image aesthetic scorers—LAION-Aesthetics, PickScore, ImageReward, and HPSv2—using pixel‑level interventions on skin tone and body type in both synthetic and real images. It finds that most scorers exhibit a fidelity preference: unaltered images receive the highest scores, while perturbations in either direction are penalized in an inverted‑U pattern, and this effect is largely independent of the skin operator. Synthetic‑only audits are misleading, as the apparent preference for darker skin in synthetic faces reverses or weakens when evaluated on real faces, and cross‑scorer results vary widely, underscoring the need for real‑data, within‑image causal isolation to accurately assess demographic bias.

By Mingyang Xu
arXiv AI
5d ago

Physiognomy Revisited: How Vision-Language Models Encode Sensitive Attributes Through Image Transformation

This paper examines how two popular vision‑language models encode protected identities when asked to transform human face images to reflect race and sexual orientation. The authors find that the models often comply with such requests, with compliance being non‑deterministic and group‑sensitive, and that the generated images are statistically distinguishable and tend to reinforce deep‑seated stereotypes. They also show that these representation biases can reappear in textual reasoning tasks and that image generation can perpetuate bias around criminality, raising concerns about AI‑assisted profiling.

By Soumyajit Datta, Arka Dutta, Ashiqur R. KhudaBukhsh