Self-supervised Multi-Modal Video Forgery Attack Detection-Reference-Cited by-同舟云学术

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Self-supervised Multi-Modal Video Forgery Attack Detection

Published:2023-03 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Wireless Communications and Networking Conference (WCNC)
language:
Short-container-title:

Author:

Zhao Chenhui¹,Li Xiang²,Younes Rabih³

Affiliation:

1. University of Michigan,Department of Electrical and Computer Engineering,Ann Arbor,USA

2. Carnegia Mellon University,Department of Electrical and Computer Engineering,Pittsburgh,USA

3. Duke University,Department of Electrical and Computer Engineering,Durham,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10118584/10118587/10118664.pdf?arnumber=10118664

Reference34 articles.

1. Mask RCNN;he;Proceedings of the IEEE International Conference on Computer Vision,2017

2. SOLO: Segmenting Objects by Locations

3. Adam: A method for stochastic optimization;kingma,2014

4. Video Instance Segmentation by Instance Flow Assembly

5. Hybrid Instance-Aware Temporal Fusion for Online Video Instance Segmentation

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