Segmentation-Based Image Copy-Move Forgery Detection Scheme

By Jian Li, Xiaolong Li, Bin Yang, Xingming Sun. IEEE Transactions on Information Forensics and Security, 2015

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Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks (2016, background): “The tasks include interactive gaming, natural language processing, image location and etc [35].”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, methodology): “Several user, content and image forensics [25] features are used as baseline features for this task.”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, methodology): “In media forensics, most of the existing forgery detection approaches focus on identifying a speciﬁc tampering method, such as copy-move [19], [36], [48], and splicing [61].”
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018, background): “Copy-move detection frameworks can be broadly classified, based on the underlying feature extraction and subsequent matching schemes, into three main categories: patch/block-based methods such as chroma features [4,9], PCA feature [14], Zernike moments [26], blur moments [20], DCT [21]; keypoint-bas”
Firefly algorithm with neighborhood attraction (2017, methodology): “To implement image segmentation [27, 49], Hassanzadeh et al.”
Effective and Efficient Global Context Verification for Image Copy Detection (2017, background): “To protect owners against unauthorized (re)use of their content, detecting illegal copies of digital multimedia is a basic requirement [1], [3], [4].”