A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD

By Guohui Li, Qiong Wu, D. Tu, ShaoJie Sun. IEEE International Conference on Multimedia and Expo, 2007

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1981429874cc18fe7c80119327c96267e3c8d401

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Citations in the corpus

Detection of Copy-Rotate-Move Forgery Using Zernike Moments (2010, background): “measured block characteristics vector from each block [4,5].”
An efficient and robust method for detecting copy-move forgery (2009, methodology): “Since brute-force search would be computationally very expensive, in [1], [2], and [3], the authors proposed to lexicographically sort the feature vectors, so that blocks with similar features would follow each other.”
An Evaluation of Digital Image Forgery Detection Approaches (2017, methodology): “Li et al. [10] proposed a copy-move forgery detection based on sorted neighborhood approach by using DWT and SVD in 2007.”
Digital image forensics: a booklet for beginners (2010, background): “Further research on robust features for segments matching is reported in [60].”
Region Duplication Detection Using Image Feature Matching (2010, neutral): “, [4], [15], [24], [26], [28], [33], [36], [41], [48]).”
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 [”
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “Most of the state-of-the-art image tamper classiﬁcation approaches utilize the frequency domain characteristics and/or statistical properties of an image [46], [56], [58], [85].”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Li et al. (2007) used singular value decomposition (SVD) for feature vector dimensionality reduction and wavelet transform for duplicated regions detection.”
Information Forensics: An Overview of the First Decade (2013, methodology): “Unlike techniques that identify copy-move forgeries through block matching, SIFT-based approaches can identify duplicate image regions even if the duplicated region has been subject to scaling, rotation, an affine transform, or illumination adjustment [31], [32].”
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] ).”
Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching (2015, methodology): “main categories: block-based algorithms [1-13] and feature key point-based algorithms [14-19].”