Image Forgery Detection using Deep Learning

Deepfake Detection: A Convolutional Neural Network Approach Digital authenticity is in grave danger due to the rapid advancement of deepfake technology.This research introduces a robust deep learning-based method to accurately detect deepfakeimages. A CNN (convolutional neural network) architecture is developed and trained on a diverse dataset of authentic and manipulated images. CNN model effectively learns discriminative features, enabling it to distinguish between genuine and forged content. The model's superior performance in identifying different deepfake techniques is demonstrated by experimental results, underscoring its potential to prevent the spread of false informationand protect digital integrity

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2024-11-06
DOI
https://doi.org/10.22214/ijraset.2024.64974
Citations
2
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
0.50
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article

Image Forgery Detection using Deep Learning

Simant Bahadur Singh
2 citations
International Journal for Research in Applied Science and Engineering Technology
Digital Media Forensic Detection
0.50
article

Image Forgery Detection using Deep Learning

Simant Bahadur Singh
article en
2 citations

Abstract

Deepfake Detection: A Convolutional Neural Network Approach Digital authenticity is in grave danger due to the rapid advancement of deepfake technology.This research introduces a robust deep learning-based method to accurately detect deepfakeimages. A CNN (convolutional neural network) architecture is developed and trained on a diverse dataset of authentic and manipulated images. CNN model effectively learns discriminative features, enabling it to distinguish between genuine and forged content. The model's superior performance in identifying different deepfake techniques is demonstrated by experimental results, underscoring its potential to prevent the spread of false informationand protect digital integrity

International Journal for Research in Applied Science and Engineering TechnologyVol. 12(11)
Reduced inequalities
Openalex Percentile: Top 34%
Digital Media Forensic Detection
0.50
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