A teleassistance protocol based on joint watermarking–encryption evidence for identification of liabilities in case of litigation

Bouslimi, Dalel; Coatrieux, Gouenou; Quantin, Catherine; Allaert, F. A.; Cozic, Michel; Roux, Christian

doi:10.1016/j.irbm.2015.07.005

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…If the embedded message is not valid or retrieved, an alarm will be raised [8]. To be efficient, these hashing or watermarking schemes need to be implemented inside the image acquisition device [9,10]. This may limit their use in real practice.…”

Section: Introductionmentioning

confidence: 99%

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints

Duan

Bouslimi

Yang

et al. 2017

IEEE J. Biomed. Health Inform.

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In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

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Section: Introductionmentioning

confidence: 99%