Efficient Dense-Field Copy–Move Forgery Detection
By Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva. IEEE Transactions on Information Forensics and Security, 2015
404 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/a66aedd895db408bd467e8688a9eec0895fd05b4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Luisa Verdoliva
- Premkumar Natarajan
- Yue Wu
- Ramkant Nevatia
- Xuefeng Hu
- Syomantak Chaudhuri
- Zhihan Zhang
- Zhenye Jiang
In Computer Vision and Pattern Recognition
- Luisa Verdoliva
- Premkumar Natarajan
- Ramkant Nevatia
- Zhenheng Yang
- Yue Wu
- Luc Van Gool
- Xuefeng Hu
- Syomantak Chaudhuri
In Media Technology
- Luisa Verdoliva
- Premkumar Natarajan
- Yue Wu
- Ramkant Nevatia
- Zhenheng Yang
- Xuefeng Hu
- Syomantak Chaudhuri
- Zhihan Zhang
Citations in the corpus
- SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization (2020, methodology): “Manipulation Detection and Localization: Most of existing methods detect and localization a specific type of manipulation only, such as copy-move[7,27,33], removal[44], enhancement[5,6], and splicing[8,16,18,29].”
- PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization (2021, neutral): “, splicing [10], [19], [30]–[36], copy-move [28], [29], [37]–[40], removal [41]–[44], and the content-preserved”
- BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization (2018, methodology): “It is worthy to mention that although [9] leads the proposed BusterNet by 7% in F1 score when only considering correctly detected samples, BusterNet correctly detected 24 more samples than [9], indicting BusterNet is better in general.”
- Media Forensics and DeepFakes: An Overview (2020, methodology): “Many datasets have been proposed specifically for copy-move forgery detection [99], [100], [103], [219], [220], the forgery most studied in the literature.”
- COVERAGE — A novel database for copy-move forgery detection (2016, methodology): “Most CMFD algorithms [1–4] are based on key-point/block-based region matching, and simply treat region similarity as a measure of copy-move forgery, ignoring ambiguity that may arise between a copy-move forged image vis-á-vis natural image with multiple similar-but-genuine objects (SGOs).”
- Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, neutral): “Image Size CoMoFod [81] 260 512× 512 Manip [23] 48 2305× 3020 GRIP [25] 100 1024× 786 COVERAGE [86] 100 400× 486 Synthesized 65k 1024× 1024”