NIST · ИТ · c2399b22d51c0ed202a6
2018 Media Forensics Challenges (MFC18): Summary and Results
The interest of forensic techniques capable of detecting many different manipulation types has been growing, and system developments with machine learning technology have been evolving in recent years. There has been, however, a lack of diverse data
NIST · ИТОфициальный источник · 2020-11-12
{
"title_en": "2018 Media Forensics Challenges (MFC18): Summary and Results",
"designation": "",
"series": "",
"status": "",
"date": "2020-11-12",
"date_original": "November 12, 2020",
"url": "https://www.nist.gov/publications/2018-media-forensics-challenges-mfc18-summary-and-results",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Yooyoung Lee , Amy Yates , Haiying Guan , Jonathan G. Fiscus , Daniel Zhou",
"abstract_excerpt_en": "The interest of forensic techniques capable of detecting many different manipulation types has been growing, and system developments with machine learning technology have been evolving in recent years. There has been, however, a lack of diverse data",
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"sources": [
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{
"title_en": "2018 Media Forensics Challenges (MFC18): Summary and Results",
"designation": "",
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"date": "2020-11-12",
"date_original": "November 12, 2020",
"url": "https://www.nist.gov/publications/2018-media-forensics-challenges-mfc18-summary-and-results",
"source": "NIST Publications",
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"authors_en": "Yooyoung Lee , Amy Yates , Haiying Guan , Jonathan G. Fiscus , Daniel Zhou",
"abstract_excerpt_en": "The interest of forensic techniques capable of detecting many different manipulation types has been growing, and system developments with machine learning technology have been evolving in recent years. There has been, however, a lack of diverse data"
}
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