Z
Zhili Zhou
Researcher at Nanjing University of Information Science and Technology
Publications - 105
Citations - 2354
Zhili Zhou is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Computer science & Steganalysis. The author has an hindex of 23, co-authored 79 publications receiving 1753 citations. Previous affiliations of Zhili Zhou include Hunan University & University of Windsor.
Papers
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Journal ArticleDOI
Effective and Efficient Global Context Verification for Image Copy Detection
TL;DR: A fast image similarity measurement based on random verification is proposed to efficiently implement copy detection and the proposed method achieves higher accuracy than the state-of-the-art methods, and has comparable efficiency to the baseline method based on the BOW quantization.
Book ChapterDOI
Coverless Image Steganography Without Embedding
TL;DR: Experimental results show that the proposed coverless image steganography framework can resist the existing steganalysis tools, and have desirable robustness to the typical image attacks such as rescaling, luminance change, and noise adding.
Journal ArticleDOI
Effective and Efficient Image Copy Detection with Resistance to Arbitrary Rotation
TL;DR: A novel effective and efficient image copy detection method is proposed based on two global features extracted from rotation invariant partitions, which can effectively and efficiently resist rotations with arbitrary degrees.
Journal ArticleDOI
Coverless image steganography using partial-duplicate image retrieval
TL;DR: Experimental results and analysis prove that this novel coverless steganographic approach without any modification for transmitting secret color image has strong resistance to steganalysis, but also has desirable security and high hiding capability.
Journal ArticleDOI
Image contrast enhancement using an artificial bee colony algorithm
TL;DR: A parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm, which outperforms conventional ABC-based image enhancement approaches.