Source camera identification based on CFA interpolation

By Sevinc Bayram, Husrev Taha Sencar, Nasir D. Memon, Ismail Avcibas. IEEE International Conference on Image Processing 2005, 2005

341 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bf13e559473138ea8efff422c20ed78b770a1f0d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Camera Model Identification Using Convolutional Neural Networks (2018, methodology): “However, software approach works with color filter array (CFA) interpolation artifacts [15], [16], [17] and Sensor Pattern Noise(SPN) [18].”
Digital image forensics: a booklet for beginners (2010, methodology): “Therefore, different demosaicing algorithms can be described based on these dependencies, as shown in [6] by Bayram et al.”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Bayram et al. (2005b) applied a technique to identify the source camera of an image based on traces of the color interpolation in the RGB color channels.”
Media Forensics and DeepFakes: An Overview (2020, background): “In addition, since CFA configuration and interpolation algorithms are specific of each camera model [61], [62], when a region is spliced in a photo taken by another camera model, its periodic pattern will appear anomalous.”
Determining Image Origin and Integrity Using Sensor Noise (2008, methodology): “In this paper, we focus on device identification and integrity verification.”
Using decision fusion of feature selection in digital forensics for camera source model identification (2012, background): “[3] [4] S Traces of CFA interpolation 95.”
Information Forensics: An Overview of the First Decade (2013, methodology): “In [82], the weighting coefficients from the EM algorithm in [78] and the peak location and magnitudes of the frequency spectrum of the probability map are used as features for classification [82].”