computer-vision-10

Video Forensics

3260 papers • 126 benchmarks • 313 datasets

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Datasets

DeeperForensics-1.0

EarthNet2021

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Most implemented papers

We Need No Pixels: Video Manipulation Detection Using Stream Descriptors

E. Delp, Paolo Bestagini, S. Tubaro, D. Güera, Sriram Baireddy•Wed Jun 19 2019

This paper proposes to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers, completely avoiding the pixel space.

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Video Face Manipulation Detection Through Ensemble of CNNs

Nicolò Bonettini, Edoardo Daniele Cannas, S. Mandelli, L. Bondi, Paolo Bestagini, S. Tubaro•Wed Apr 15 2020

This paper studies the ensembling of different trained Convolutional Neural Network (CNN) models and shows that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.

259 0

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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

M. Reichstein, Joachim Denzler, Christian Requena-Mesa, V. Benson, J. Runge•Thu Apr 15 2021

This work frames Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather, which allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring.

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DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

Chen Change Loy, Chen Qian, Wayne Wu, Liming Jiang, Ren Li•Wed Jan 08 2020

The on-going effort of constructing a large- scale benchmark for face forgery detection is presented, with 60, 000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind.

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H4VDM: H.264 Video Device Matching

E. Delp, Paolo Bestagini, S. Tubaro, Ziyue Xiang•Wed Oct 19 2022

This paper proposes a technique that can determine if two given video sequences are captured by the same device, even if the method has never encountered the device in training, and denotes it as H.264 Video Device Matching (H4VDM).

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MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

Paolo Bestagini, S. Tubaro, Ziyue Xiang, Ajay Kumar, Singh Yadav, Edward J. Delp•Wed May 31 2023

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods.

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