DEMA: decentralized electrical network frequency map for social media authentication
DEMA: decentralized electrical network frequency map for social media authentication
复制标题
DEMA:用于社交媒体认证的去中心化电网频率图
DOI:
10.1117/12.2663303
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Chen, Yu
中科院分区:
文献类型:
--
作者:
Nagothu, Deeraj;Xu, Ronghua;Chen, Yu
The information era has gained a lot of traction due to the abundant digital media contents through technological broadcasting resources. Among the information providers, the social media platform has remained a popular platform for the widespread reach of digital content. Along with accessibility and reach, social media platforms are also a huge venue for spreading misinformation since the data is not curated by trusted authorities. With many malicious participants involved, artificially generated media or strategically altered content could potentially result in affecting the integrity of targeted organizations. Popular content generation tools like DeepFake have allowed perpetrators to create realistic media content by manipulating the targeted subject with a fake identity or actions. Media metadata like time and location-based information are altered to create a false perception of real events. In this work, we propose a Decentralized Electrical Network Frequency (ENF)-based Media Authentication (DEMA) system to verify the metadata information and the digital multimedia integrity. Leveraging the environmental ENF fingerprint captured by digital media recorders, altered media content is detected by exploiting the ENF consistency based on its time and location of recording along with its spatial consistency throughout the captured frames. A decentralized and hierarchical ENF map is created as a reference database for time and location verification. For digital media uploaded to a broadcasting service, the proposed DEMA system correlates the underlying ENF fingerprint with the stored ENF map to authenticate the media metadata. With the media metadata intact, the embedded ENF in the recording is compared with a reference ENF based on the time of recording, and a correlation-based metric is used to evaluate the media authenticity. In case of missing metadata, the frames are divided spatially to compare the ENF consistency throughout the recording.
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DOI:
10.1109/mmsp53017.2021.9733503
发表时间:
2021
期刊:
2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)
影响因子:
--
作者:
Deeraj Nagothu;Ronghua Xu;Yu Chen;Erik Blasch;Alexander J. Aved
通讯作者:
Alexander J. Aved
DOI:
--
发表时间:
2022
期刊:
The Web Conference
影响因子:
--
作者:
Dhruv Kuchhal;Frank Li
通讯作者:
Frank Li
DOI:
10.4108/eai.4-2-2021.168648
发表时间:
2018
期刊:
EAI Endorsed Trans. Security Safety
影响因子:
--
作者:
Deeraj Nagothu;Yu Chen;Alexander J. Aved;E. Blasch
通讯作者:
E. Blasch
DOI:
--
发表时间:
2015
期刊:
Information Hiding and Multimedia Security Workshop
影响因子:
--
作者:
Adi Hajj;Séverine Baudry;B. Chupeau;G. Doërr
通讯作者:
G. Doërr
DOI:
--
发表时间:
2021
期刊:
影响因子:
--
作者:
Ravi Garg;Adi Hajj;Min Wu
通讯作者:
Min Wu