computer-vision-7

Image and Video Forgery Detection

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

J. Yamagishi, H. Nguyen, I. Echizen•Thu Oct 25 2018

The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer-generated videos using deep convolutional neural networks.

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Use of a Capsule Network to Detect Fake Images and Videos

J. Yamagishi, H. Nguyen, I. Echizen•Sun Oct 27 2019

A capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning, uses many fewer parameters than traditional convolutional neural networks with similar performance.

214 0

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AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics

Mingzhen Huang, Shan Jia, Siwei Lyu, Zhou Zhou, Yan Ju, Jialing Cai•Thu Apr 13 2023

This paper proposes a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt, which has resulted in the creation of a new image dataset called AutoSplice, containing 5,894 manipulated and authentic images.

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