International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Image Forgery Detection Based on Fusion of Light Weight Deep Learning Models

Author(s) Sowmya shree A, Manjunatha Kumar B H, Seshaiah M
Country India
Abstract Image forgery detection is among the vital difficulties in different ongoing applications, virtual entertainment, and online data stages. The ordinary techniques for location considering the hints of picture controls are restricted Up to predetermined limits that involve handmade highlights, disparities, and size. In this study, we offer a choice approach to picture creation identification based on pairings. The lightweight profound learning models, especially Crush Net, MobileNetV2, and Mix Net, establish the combination that should be used. The Combination choice framework is executed in two stages. To start With, the pre prepared loads of the light-weight advanced learning models are employed to assess the falsification of the pictures. Furthermore, the tweaked loads are employed to analyze the after effects among the fraud of the pictures using the prior-prepared prototypes. The exploratory outcomes recommend the fact that the combination -founded choice methodology accomplishes superior precision when in contrast to the cutting edge draws near.
Keywords picture forensics, image forgery detection, deep comprehension, convolutional neural network
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-07-05
Cite This Image Forgery Detection Based on Fusion of Light Weight Deep Learning Models - Sowmya shree A, Manjunatha Kumar B H, Seshaiah M - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.23999
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.23999
Short DOI https://doi.org/gt3nf8
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