May 15, 2024, 4:11 a.m. | Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, Kede Ma

cs.CR updates on

arXiv:2405.08487v1 Announce Type: cross
Abstract: In recent years, deep learning has greatly streamlined the process of generating realistic fake face images. Aware of the dangers, researchers have developed various tools to spot these counterfeits. Yet none asked the fundamental question: What digital manipulations make a real photographic face image fake, while others do not? In this paper, we put face forgery in a semantic context and define that computational methods that alter semantic face attributes to exceed human discrimination thresholds …

arxiv aware dataset deep learning definition detection digital fake forgery images process question real researchers semantic tools

Sr. Product Manager

@ MixMode | Remote, US

Information Security Engineers

@ D. E. Shaw Research | New York City

Technology Security Analyst

@ Halton Region | Oakville, Ontario, Canada

Senior Cyber Security Analyst

@ Valley Water | San Jose, CA

Security Operations Analyst, Senior

@ Booz Allen Hamilton | USA, WV, Clarksburg (1000 Custer Hollow Rd)

Security Solution Consultant

@ Genesys | Durham (Flexible)