W
Wen Chen
Researcher at New Jersey Institute of Technology
Publications - 13
Citations - 1465
Wen Chen is an academic researcher from New Jersey Institute of Technology. The author has contributed to research in topics: Steganalysis & Wavelet. The author has an hindex of 10, co-authored 11 publications receiving 1390 citations.
Papers
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Book ChapterDOI
A Markov process based approach to effective attacking JPEG steganography
Yun Q. Shi,Chunhua Chen,Wen Chen +2 more
TL;DR: A novel steganalysis scheme is presented to effectively detect the advanced JPEG steganography and has outperformed the existing steganalyzers in attacking OutGuess, F5, and MB1.
Proceedings ArticleDOI
A natural image model approach to splicing detection
Yun Q. Shi,Chunhua Chen,Wen Chen +2 more
TL;DR: A blind, passive, yet effective splicing detection approach based on a natural image model that consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT).
Proceedings ArticleDOI
Identifying Computer Graphics using HSV Color Model and Statistical Moments of Characteristic Functions
Wen Chen,Yun Q. Shi,Guorong Xuan +2 more
TL;DR: A novel approach to distinguishing computer graphics from photographic images is introduced, using the statistical moments of characteristic function of the image and wavelet subbands as the distinguishing features and the influence of different image color representations on the feature effectiveness is investigated.
Proceedings ArticleDOI
Image splicing detection using 2-D phase congruency and statistical moments of characteristic function
Wen Chen,Yun Q. Shi,Wei Su +2 more
TL;DR: The proposed scheme extracts image features from moments of wavelet characteristic functions and 2-D phase congruency for image splicing detection and can achieve a higher detection rate as compared with the state-of-the-art.
Book ChapterDOI
Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions
Guorong Xuan,Yun Q. Shi,Jianjiong Gao,Dekun Zou,Chengyun Yang,Zhenping Zhang,Peiqi Chai,Chunhua Chen,Wen Chen +8 more
TL;DR: The theoretical analysis has pointed out that the defined n-th statistical moment of a wavelet characteristic function is related to the n- fourth derivative of the corresponding wavelet histogram, and hence is sensitive to data embedding.