Sensor-fingerprint based identification of images corrected for lens distortion

Computational photography is quickly making its way from research labs to the market. Recently, camera manufacturers started using in-camera lens-distortion correction of the captured image to give users more powerful range of zoom in compact and affordable cameras. Since the distortion correction (barrel/pincushion) depends on the zoom, it desynchronizes the pixel-to-pixel correspondence between images taken at two different focal lengths. This poses a serious problem for digital forensic methods that utilize the concept of sensor fingerprint (photo-response non-uniformity), such as "image ballistic" techniques that can match an image to a specific camera. Such techniques may completely fail. This paper presents an extension of sensor-based camera identification to images corrected for lens distortion. To reestablish synchronization between an image and the fingerprint, we adopt a barrel distortion model and search for its parameter to maximize the detection statistic, which is the peak to correlation energy ratio. The proposed method is tested on hundreds of images from three compact cameras to prove the viability of the approach and demonstrate its efficiency.

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