Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

By Chi-Man Pun, Xiaochen Yuan, Xiu-Li Bi. IEEE Transactions on Information Forensics and Security, 2015

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Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “Over the past decades, there have been many works to classify image manipulation, i.e., whether an image is tampered or not [13], [30], [39], [47], [65], [74], [78].”
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”