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pro vyhledávání: '"Rosenberg, Harrison"'
Computer vision systems have been deployed in various applications involving biometrics like human faces. These systems can identify social media users, search for missing persons, and verify identity of individuals. While computer vision models are
Externí odkaz:
http://arxiv.org/abs/2407.13922
Autor:
Rosenberg, Harrison, Ahmed, Shimaa, Ramesh, Guruprasad V, Vinayak, Ramya Korlakai, Fawaz, Kassem
Text-to-image diffusion models have achieved widespread popularity due to their unprecedented image generation capability. In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both
Externí odkaz:
http://arxiv.org/abs/2309.07277
Recent works have investigated the sample complexity necessary for fair machine learning. The most advanced of such sample complexity bounds are developed by analyzing multicalibration uniform convergence for a given predictor class. We present a fra
Externí odkaz:
http://arxiv.org/abs/2202.04530
The proliferation of automated face recognition in the commercial and government sectors has caused significant privacy concerns for individuals. One approach to address these privacy concerns is to employ evasion attacks against the metric embedding
Externí odkaz:
http://arxiv.org/abs/2108.02707
Recent advances in machine learning (ML) algorithms, especially deep neural networks (DNNs), have demonstrated remarkable success (sometimes exceeding human-level performance) on several tasks, including face and speech recognition. However, ML algor
Externí odkaz:
http://arxiv.org/abs/2003.01595
State-of-the-art machine learning models frequently misclassify inputs that have been perturbed in an adversarial manner. Adversarial perturbations generated for a given input and a specific classifier often seem to be effective on other inputs and e
Externí odkaz:
http://arxiv.org/abs/1811.03531