Detection of Copy-Move Forgery in Digital Images

By Jessica J. Fridrich. 2004

1,317 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6be1c27bb9f7f45854af8dcd4b2d3217d6bd71c5

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Citations in the corpus

Region Duplication Detection Using Image Feature Matching (2010, unknown): “, [4], [15], [24], [26], [28], [33], [36], [41], [48]).”
Exposing digital forgeries by detecting inconsistencies in lighting (2005, background): “, [4, 5, 10, 11]), will make it increasingly harder (but never impossible) to create convincing digital forgeries.”
Forensic detection of image manipulation using statistical intrinsic fingerprints (2010, background): “Additionally, the efficient identification of copy and move forgeries has been studied [ 18 ].”
An efficient parts-based near-duplicate and sub-image retrieval system (2004, background): “Our system achieves nearperfect accuracy (100% precision at 99.85% recall) on the tests presented in Meng et al. [16], and consistently strong results on our own, signi.cantly more challenging experiments.”
Image forgery detection (2009, methodology): “Two computationally efficient algorithms have been developed to detect cloned image regions ( [11], [34] ; see also [23], [27], and [42] ).”
An Evaluation of Digital Image Forgery Detection Approaches (2017, methodology): “Fridrich et al. [13] proposed a method for identifying copy-move image forgery in 2003.”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “First attempt in identifying tampered areas was investigated by Fridrich et al. (2003).”
Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching (2015, methodology): “Of the existing types of image tampering, a common manipulation of a digital image is copy-move forgery [1], which is to paste one or several copied region(s) of an image into other part(s) of the same image.”
Media Forensics and DeepFakes: An Overview (2020, methodology): “Studies on copy-move detection date back to 2003, with the seminal work of Fridrich [90].”
Determining Image Origin and Integrity Using Sensor Noise (2008, background): “Under , comes from the tested camera and we have or (12) Since is zero mean and independent of , the mixed term is small compared to the other terms in the denominator of (12).”
Copy-move forgery detection and localization by means of robust clustering with J-Linkage (2013, methodology): “Different block-based representations have been previously proposed in the literature such as principal component analysis (PCA) [8] [9], discrete cosine transform (DCT) [10] and discrete wavelet transform (DWT) [11] [12], for both tasks of copy-move detection [10] [11] [13] [9] and image splicing [”
Robust Detection of Region-Duplication Forgery in Digital Image (2006, methodology): “Compared with the methods in [4] and [7], our algorithm has lower computational complexity and is more robust against stronger attacks and various types of after-copying manipulations, such as lossy compressing, noise contamination, blurring and a combination of these operations.”
Efficient Near-duplicate Detection and Sub-image Retrieval (2004, background): “For instance, one could crop political figures from two different photographs and create a fake composite image showing them shaking hands, even though the individuals may never have met in reality [6].”
An efficient and robust method for detecting copy-move forgery (2009, methodology): “We also implemented the methods, described in [1], and [2] and compared the robustness of these three methods.”
Segmentation-Based Image Copy-Move Forgery Detection Scheme (2015, methodology): “In [2] the authors proposed such a kind of method based on DCT describing the block, and they also decreased the complexity of the matching process by means of dictionary sorting.”
Detection of copy-move forgery using a method based on blur moment invariants. (2007, methodology): “This method differs from [7] mainly in the representation of overlapping image blocks.”
A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD (2007, methodology): “Fridrich[3] proposes an effective way to detect copy-move forgery blocks in a single image.”
Exposing Digital Forgeries From JPEG Ghosts (2009, methodology): “Fig. 1. Shown in panel (a) is the sum of squared differences between coefficients quantized by an amount q1 = 17, followed by a second quantization in the range q2 2 [ 1 ,30] (horizontal axis) ‐ this difference reaches a minimum at q2 = q1 = 17. Shown in panel (b) is the sum of squared differences b”
Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact (2007, background): “The first attempt is to detect the traces of specific manipulation during forgery creation, such as resampling [1], copypaste [2, 3], and color filter array interpolation [4].”
Exposing Digital Forgeries in Complex Lighting Environments (2007, methodology): “Statistical techniques have been developed for detecting cloning [1], [2]; splicing [3]; resampling artifacts [4], [5]; color filter array aberrations [4]; and disturbances of a camera’s sensor noise pattern [6].”