Journal Article10.1109/mipro60963.2024.10569232
Enhancing Digital Image Forensics with Error Level Analysis (ELA)
Robert Idlbek,Mirko Pešić,Krešimir Šolić +2 more
- 20 May 2024
About: The article was published on 20 May 2024. The article focuses on the topics: Computer science & Digital forensics.
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Variational image compression with a scale hyperprior
Johannes Ballé,David Minnen,Saurabh Singh,Sung Jin Hwang,Nick Johnston +4 more
- 01 Feb 2018
TL;DR: In this paper, an end-to-end trainable model for image compression based on variational autoencoders is proposed, which incorporates a hyperprior to effectively capture spatial dependencies in the latent representation.
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The 'Dresden Image Database' for benchmarking digital image forensics
Thomas Gloe,Rainer Böhme +1 more
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TL;DR: A novel image database specifically built for the purpose of development and bench-marking of camera-based digital forensic techniques and is intended to become a useful resource for researchers and forensic investigators.
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RAISE: a raw images dataset for digital image forensics
Duc-Tien Dang-Nguyen,Cecilia Pasquini,V. Conotter,Giulia Boato +3 more
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TL;DR: How RAISE has been collected and organized is described, how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and a very recent forensic technique for JPEG compression detection is tested.
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Development of photo forensics algorithm by detecting photoshop manipulation using error level analysis
Teddy Surya Gunawan,Siti Amalina Mohammad Hanafiah,Mira Kartiwi,Nanang Ismail,Nor Farahidah Za'bah,Anis Nurashikin Nordin +5 more
TL;DR: The objective of this paper is to develop a photo forensics algorithm which can detect any photo manipulation and showed that the proposed algorithm could identify successfully the modified image as well as showing the exact location of modifications.
Deep fake detection and classification using error-level analysis and deep learning
TL;DR: In this paper , the authors proposed an automated method to classify deep fake images by employing deep learning and machine learning based methodologies, which achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor.