Detecting digital image forgeries using sensor pattern noise

Abstract

We present a new approach to detection of forgeries in digital images under the assumption that either the camera that took the image is available or other images taken by that camera are available. Our method is based on detecting the presence of the camera pattern noise, which is a unique stochastic characteristic of imaging sensors, in individual regions in the image. The forged region is determined as the one that lacks the pattern noise. The presence of the noise is established using correlation as in detection of spread spectrum watermarks. We proposed two approaches. In the first one, the user selects an area for integrity verification. The second method attempts to automatically determine the forged area without assuming any a priori knowledge. The methods are tested both on examples of real forgeries and on non-forged images. We also investigate how further image processing applied to the forged image, such as lossy compression or filtering, influences our ability to verify image integrity.

Exposing Digital Forgeries From JPEG Ghosts

“…These include techniques for detecting cloning [1], [2]; splicing [3]; re-sampling artifacts [4], [5] ; color filter array aberrations [6]; disturbances of a camera's sensor noise pattern [7]; chromatic aberrations [8]; and lighting inconsistencies [9], [10], [11]. Although highly effective in some situations, many of these techniques are only applicable to relatively high quality images.…”

Detecting Video Forgeries Based on Noise Characteristics

“…They extracted fixed pattern noise from a given image using a smoothing filter and identified the camera that took the image. The authors also proposed a method for detecting forgeries in an image using the same approach [10]. This paper introduces a video forensic method by checking for inconsistency of the noise characteristics, which has never been proposed among the forensic methods for videos.…”

“…Lin et al estimated camera response function and verified its uniformity across an image [7]. Lukáš et al extracted fixed pattern noise from an image and compared it with a reference pattern [10]. Fridrich et al computed correlation between segments in an image and detected cloned regions [2].…”

Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation

“…The aforementioned works lead to detection schemes based on demosaicking inconsistency [2,3], sensor noise [8], CRF abnormality [9], and CRF inconsistency [10].…”