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

Duan, Yuping; Bouslimi, Dalel; Yang, Guanyu; Hu, Shu; Coatrieux, Gouenou

doi:10.1109/jbhi.2016.2575398

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…The second one determines an acquisition system based on the way that its three-dimensional (3-D) image reconstruction algorithm modifies this noise. Since these reconstruction algorithms depend on the manufacturer and are confidential, their function will be used as input to train a support vector machine (SVM) based classifier to distinguish the acquisition system [3]. His method cannot guarantee the accuracy of processing a large number of pictures.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

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Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This paper mainly studies the forest image recognition system based on CCD and theodolite. In this paper, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this paper, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

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Section: Introductionmentioning

confidence: 99%

Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

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“…al. [19] has proposed identification of CT scanner. The first aim was to identify original sensor pattern noise.…”

Section: Literature Reviewmentioning

confidence: 99%

Source Identification of Videos Transmitted in Lossy Wireless Networks

Kaur¹,

Randhawa²

2017

International Journal of Innovative Research in Electrical, Ele

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Abstract:The sensor pattern noise based method exploits the inherent fingerprint of the camera sensor and is universally applicable. This method has the best performance reported so far among the existing methods and only method effective in lossy wireless network incorporating wireless channel characteristics and protection from security threats like blurring, blocking and spoofing attack. Experiment test was conducted with 20 test each on 8 videos of eight wireless cameras taking randomly 5 frames of the video to be tested using Sensor Pattern Noise based on correlation based coeffient matching and our proposed work based on Gaussian Mixture Model matching. It leads us to conclusion that it is more reliable, accurate and less Complex. It is applicable on different size video, requirement of random frames is very less as compared to existed work and help to create trained data set only once.

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“…Passive digital content forensics techniques constitute an interesting alternative. Basically, these methods are based on the fact that each device (flatbed scanner, CT scanner, digital camera, printer...) leaves unique traces in the digital images it produces [3]. These traces can be retrieved in scanned and exploited as a unique scanner fingerprint for SSI.…”

Section: Introductionmentioning

confidence: 99%

Boosting up Source Scanner Identification Using Wavelets and Convolutional Neural Networks

Rabah¹,

Coatrieux²,

Abdelfattah³

2020

Self Cite

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In this paper, we present a conceptually innovative method for source scanner identification (SSI), that is to say, identifying the scanner at the origin of a scanned document. Solutions from literature can distinguish between scanners of different brands and models but fail to differentiate between scanners of the same models. To overcome this issue, the approach we propose takes advantage of a convolutional neural network (CNN) to automatically extract intrinsic scanner features from the distribution of the coefficients of the diagonal high-frequency (HH) sub-band of the discrete stationary wavelet transform (SWT) of scanned images. Such information serves as a reliable characteristic to classify scanners of different/same brands and models. Experiments conducted on a set of 8 scanners yielded a model with an accuracy of 99.31% at the block level and 100% at the full image level, showcasing the potential of using deep learning for SSI and outperforming existing schemes from literature. The influence of the model’s parameters such as the input size, the training data size, the number of layers, and the number of nodes in the fully connected layer as well as the effect of the pre-processing step were investigated.

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

Cited by 7 publications

References 29 publications

Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Retracted: Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Source Identification of Videos Transmitted in Lossy Wireless Networks

Boosting up Source Scanner Identification Using Wavelets and Convolutional Neural Networks

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