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ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Penerapan Generative Adversarial Network Pada Footage Forensik Digital

Authors: Ridha Putri, Muhammad Fauzan Azima, Rionaldi Ali;

Penerapan Generative Adversarial Network Pada Footage Forensik Digital

Abstract

Footage dalam dunia forensik digital selalu datang dalam keadaan tidak bagus seperti buram, kualitas rendah, bahkan tidak jelas. Padahal dalam forensik digital yang di butuhkan adalah informasi dan kejelasan dari bukti tersebut. Terdapat kebutuhan mendesak dari bidang forensik yang seringkali memerlukan citra digital yang berkualitas tinggi, terutama dalam situasi di mana terdapat suatu kejahatan dengan barang bukti jejak digital contohnya berupa citra digital. Dengan menggunakan GANs memiliki kemampuan untuk merekonstruksi citra dari kualitas rendah menjadi berkualitas tinggi, cocok untuk mengatasi masalah dalam forensik digital seperti Gaussian blur, citra berkualitas rendah, buram, dan pixelated. Peningkatan kualitas citra, terutama pada wajah, memperjelas objek dan subjek dalam citra, memudahkan proses identifikasi dalam penyelidikan forensik. GANs juga dapat merekonstruksi lingkungan sekitar citra dengan baik. Seluruh ukuran citra dapat direstorasi menggunakan GANs. Penelitian menetapkan faktor skala tinggi (2x2 dan 4x4) untuk memperbesar citra, meningkatkan resolusi secara signifikan. Diperlukan studi lebih lanjut untuk memperluas cakupan GFPGAN, seperti restorasi citra non-wajah dan pemulihan citra dalam konteks lainnya. Gunakan perangkat keras yang memiliki ruang penyimpanan besar untuk mempercepat proses rekontruksi.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average