Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

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Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Some works have shown that it is possible to identify the camera taking the photos (and thus link photos and events via the photographer), either from the file itself [7] or from recognisable sensing noise [8, 9].”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Dirik et al. (2008) proposed a source camera identification method based on detection and matching of the sensor dust characteristics with average 99% identification accuracy.”
Camera Model Identification Using Convolutional Neural Networks (2018, background): “Hardware category considers features of camera hardware, such as lens [12], [13] and harge Coupled Device (CCD) sensors [14].”
Using decision fusion of feature selection in digital forensics for camera source model identification (2012, methodology): “When the method of detecting intrinsic hardware artifacts is adopted, artifacts such as pattern noise [18,21,22,26], lens radial distortion [11], chromatic aberration [44] or sensor dust [16] are used as the fingerprint or biometric to identify either the brand/model source or device source.”