Study on anti-forensics of single and double JPEG detection using convolutional neural network컨볼류션 신경망을 이용한 단일 및 이중 JPEG 압축 탐지 안티포렌식 연구

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 90
  • Download : 0
JPEG compression is one of the major image compression methods and is widely used on the Internet. In addition, identifying traces of JPEG compression and double JPEG compression (DJPEG) is crucial in the image forensics field. Therefore, JPEG compression detection and DJPEG compression detection are two of the popular image authentication methods. Many feature-based JPEG detection methods have been proposed for that purpose, and there have been outstanding improvements in DJPEG detection with the development of deep learning. A number of anti-forensics of JPEG detection that counter feature-based detectors have been proposed but only a few techniques that counter DJPEG have been researched. This paper explores whether JPEG reconstruction methods, including restoration and anti-forensics of JPEG detection, can deceive JPEG and DJPEG detectors. We demonstrate that existing anti-forensics of JPEG detection can deceive both JPEG and DJPEG detectors well but perform poorly in non-aligned cases and degrade the image quality. We propose a convolutional neural network (CNN) based anti-forensics method to improve the performance of anti-forensics so that they can proficiently deceive JPEG and DJPEG detectors with higher image quality.
Advisors
Lee, Heungkyuresearcher이흥규researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2021
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2021.2,[iv, 33 p. :]

Keywords

Anti-forensics▼aJPEG detection▼aDJPEG detection▼aJPEG Restoration▼aCNN; 안티포렌식▼aJPEG 탐지▼aDJPEG 탐지▼aJPEG 리스토레이션▼aCNN

URI
http://hdl.handle.net/10203/296146
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=957314&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0