콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2018-02
- 저자명 장한얼
- 학술지명 IEEE SIGNAL PROCESSING LETTERS
- 발행처명 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 5
논문 초록 (Abstract)
Median filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.
