Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Autor: Israr Hussain, Dostdar Hussain, Rashi Kohli, Muhammad Ismail, Saddam Hussain, Syed Sajid Ullah, Roobaea Alroobaea, Wajid Ali, Fazlullah Umar
Rok vydání: 2022
Předmět:
Zdroj: Mobile Information Systems
ISSN: 1875-905X
1574-017X
DOI: 10.1155/2022/2847580
Popis: Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.
Databáze: OpenAIRE
Externí odkaz: