Deep fake detection using a sparse auto encoder with a graph capsule dual graph CNN

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019237" target="_blank" >RIV/62690094:18470/22:50019237 - isvavai.cz</a>

Result on the web

<a href="https://peerj.com/articles/cs-953/#" target="_blank" >https://peerj.com/articles/cs-953/#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj-cs.953" target="_blank" >10.7717/peerj-cs.953</a>

Result language

angličtina

Original language name

Deep fake detection using a sparse auto encoder with a graph capsule dual graph CNN

Original language description

Deepfake (DF) is a kind of forged image or video that is developed to spread misinformation and facilitate vulnerabilities to privacy hacking and truth masking with advanced technologies, including deep learning and artificial intelligence with trained algorithms. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. This kind of multimedia manipulation, such as changing facial expressions or speech, can be used for a variety of purposes to spread misinformation or exploitation. With the recent advancement of generative adversarial networks (GANs) in deep learning models, DF has become an essential part of social media. To detect forged video and images, numerous methods have been developed, and those methods are focused on a particular domain and obsolete in the case of new attacks/threats. Hence, a novel method needs to be developed to tackle new attacks. The method introduced in this article can detect various types of spoofs of images and videos that are computationally generated using deep learning models, such as variants of long short-term memory and convolutional neural networks. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The first phase of this proposed work extracts the feature frames from the forged video/image using a sparse autoencoder with a graph long short-term memory (SAE-GLSTM) method at training time. The proposed DF detection model is tested using the FFHQ database, 100K-Faces, Celeb-DF (V2) and WildDeepfake. The evaluated results show the effectiveness of the proposed method.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PeerJ Computer Science

ISSN

2376-5992

e-ISSN

2376-5992

Volume of the periodical

8

Issue of the periodical within the volume

May

Country of publishing house

GB - UNITED KINGDOM

Number of pages

21

Pages from-to

"Article Number: e953"

UT code for WoS article

000811320500002

EID of the result in the Scopus database

2-s2.0-85131916851