Е.А. Aminova – Post-graduate Student, P.G. Demidov Yaroslavl State University
I.N. Trapeznikov – Ph.D. (Eng.), P.G. Demidov Yaroslavl State University
А.L. Priorov – Dr.Sc. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University
Yu.A. Bryukhanov – Dr.Sc (Eng.), Professor, P.G. Demidov Yaroslavl State University
In this article, the solution of the authenticity-verifying problem digital image under from the supposed DSLR camera is proposed. This task is considered in the context of the image formation process in digital devices and the arising noise and interference at each stages of this formation. As the basis of the solution, it is proposed to use the structural noise of a digital device, which is unique for each particular device. This type of noise is caused by uneven response of the sensor pixels to the incident light.
Firstly, a unique noise distribution of the structural interference of each device is formed on the basis of several images obtained from device under consideration. Further, based on the correlation analysis, this distribution is compared to that obtained from the test image. Depending on the obtained value of the correlation metric, decisions are made on the formation of a test image by the intended image source.
The dependence of the quality of the proposed algorithm on the parameters of input images is analyzed: the size, the format, the number of images in the set to form the noise distribution, the computing costs of the algorithm and the quality of the JPEG image.
During the testing it was revealed that a sufficient number of images used for the device imperfections distribution without a significant decrease in performance is 15. It is also shown that the algorithm is applicable for various formats and sizes of digital images. Since the JPEG compression format is most often used in modern DSLR devices, the dependence of the correlation metric value on the quality of JPEG compression has been carried out. The steadiness of the algorithm is achieved with quality digital image values from 75% from original relative to the best.
To sum up, the algorithm is applicable for various formats and sizes of digital images.
- Geradts Z.J., Bijhold J., Kieft M., Kurosawa K., Kuroki K., Saitoh N. Methods for identification of images acquired with digital cameras // Enabling Technologies for Law Enforcement. 2001. V. 4232. P. 505–512.
- Alsop R. Workflow Automation Integration Requires A Large Technology Toolkit and A Structured Approach // Computer Technology Review, 1994.
- Black D. Workflow Software: A Layman’s Handbook, Part I. 1994. Inform.
- Boutell M., Luo J. Photo classification by integrating image content and camera metadata // Pattern recognition 17th international conference (ICPR 04). 2004. V. 4. P. 901–904.
- Chapman G.H., Leung J., Namburete A., Koren I., Koren Z. Predicting pixel defect rates based on image sensor parameters // IEEE International Symposium on Defect and Fault Tolerance. Vancouver. Canada. P. 408–416.
- Holst G.C. CCD Arrays, Cameras, and Displays. 2nd edition // JCD Publishing & SPIE Pres. USA. 1998.
- Kharrazi M., Sencar H.T., Memon N. Blind Source Camera Identification // ICIP-04. Singapore. P. 24–27.
- Janesick J.R. Scientific Charge-Coupled Devices // SPIE PRESS Monograph, SPIE–The International Society for Optical Engineering. 2001. V. PM83.
- Gill P.R., Lee C., Lee D.-G., Wang A., Molnar A. A microscale camera using direct Fourier-domain scene capture // Optics Letters. 2011. V. 36. № 15. P. 2949–2951.
- Aminova E.A., Trapeznikov I.N., Priorov A.L. Overview of digital forensics algorithms in DSLR cameras // International Workshop Photogrammetric and Computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine. 2017. P. 199–205.
- Mihcak M.K., Kozintsev I., Ramchandran K. Spatially Adaptive Statistical Modeling of Wavelet Image Coefficientsand its Application to Denoising // IEEE Int. Conf. Acoustics, Speech, and Signal Processing. 1999. V. 6. P. 3253–3256.
- Becker H.N., Alexander J.W., Dolphin M.D., Eisenman A.R., Salomon P.M., Selva L.E., Thorbourn D.O. Commercial Sensor Survey Fiscal Year 2009 Master Compendium Radiation Test Report // Jet Propulsion Laboratory California Institute of Technology. California. 2010.
- Khan S., Kulkarni A. Detection of copy-move forgery using multiresolution characteristic of discrete wavelet transform // International Conference and Workshop Emerging Trends in Technologies. (ICWET). 2015. P. 127–131.
- Kurosawa K., Kuroki K., Saitoh N. CCD Fingerprint Method – Identification of a Video Camera from Videotaped Images // International conference on image Processing. 1999. P. 537–540.
- Goljan M., Fridrich J., Filler T. Large Scale Test of Sensor Fingerprint Camera Identification // Electronic Imaging, Media Forensics and Security XI (SPIE). 2009. V. 7254. P. 0I–0J.
- Bayram S., Sencar H.T., Memon N. An efficient and robust method for detecting copy-move forgery // IEEE International Conference Acoustics, Speech Signal Processing (ICASSP). 2015. P. 1053–1056.
- Ghorbani M., Firouzmand M., Faraahi A. DWT-DCT (QCD) based copy-move image forgery detection // 18th International Conference Systems, Signals Image Processing (IWSSIP). 2011. P. 1–4.
- Ziv J., Lempel A. A Universal Algorithm for Sequential Data Compression // IEEE Transactions on Information Theory. 1977. V. 23. № 3. P. 337–343.
- Welsh T. A Technique for High-Performance Data Compression // IEEE Computer. 1984. V. 17. P. 8–19.
- Krishnan D., Fergus R. Dark flash photography // ACM Trans. on Graphics. 2009. V. 28. P. 1–8.