Review
Version 1
Preserved in Portico This version is not peer-reviewed
Review on Image Forensic Techniques Based on Deep Learning
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These authors contributed equally to this work.
Version 1
: Received: 15 June 2023 / Approved: 16 June 2023 / Online: 16 June 2023 (11:07:50 CEST)
A peer-reviewed article of this Preprint also exists.
Shi, C.; Chen, L.; Wang, C.; Zhou, X.; Qin, Z. Review of Image Forensic Techniques Based on Deep Learning. Mathematics 2023, 11, 3134. Shi, C.; Chen, L.; Wang, C.; Zhou, X.; Qin, Z. Review of Image Forensic Techniques Based on Deep Learning. Mathematics 2023, 11, 3134.
Abstract
Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.
Keywords
image forensics; image forgery detection; robust image watermarking; deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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