Article citationsMore>>

Rao, Y. and Ni, J.Q. (2016) A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images. 2016 IEEE International Workshop on Information Forensics and Security, Abu Dhabi, 4-7 December 2016, 2157-4774.
https://doi.org/10.1109/WIFS.2016.7823911

has been cited by the following article:

• TITLE: A Survey of Blind Forensics Techniques for JPEG Image Tampering

  AUTHORS: Xueling Chu, Haiming Li

  KEYWORDS: Image Forensics, Tamper Detection, JPEG Image Forensics, JPEG Block Effect

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.10, October 11, 2019

  ABSTRACT: Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described. Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection. This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods. Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.