Radon transform of image monotonic rearrangements as feature for noise sensor signature

被引:1
|
作者
Bruni, Vittoria [1 ]
Marconi, Silvia [1 ]
Monteverde, Giuseppina [1 ]
Vitulano, Domenico [1 ]
机构
[1] Sapienza Univ Rome, Dept Basic & Appl Sci Engn, Via Antonio Scarpa 16, I-00161 Rome, Italy
关键词
Radon transform; Function monotonic rearrangements; Source camera identification; PRNU; DIGITAL CAMERA IDENTIFICATION; PRNU; FORENSICS;
D O I
10.1016/j.amc.2023.128173
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Source camera identification represents a delicate, crucial but challenging task in digital forensics, especially when an image has to be used as a proof in a court of law. This paper investigates some properties of the Photo Response Non Uniformity (PRNU) pattern noise that represents the fingerprint of any acquisition sensor. The main goal is to define specific and distinctive features for this noise source that enable the identification of the acquisition sensor by simply analysing a few images. These features are required to be independent of image size, modifications, storage mode, etc. The discrimination power of the decreasing rearrangement of a function, combined with the Radon transform, has been investigated in this paper. Preliminary tests show that a proper rearrangement of PRNU image provides specific and device-dependent geometric structures that can be properly coded through the Radon transform. In particular, the empirical distribution of the Radon Transform of rearranged Flat Field images alone is capable to correctly characterize each device with high accuracy, showing robusteness to some standard image modifications, such as quantization and blurring; in addition, it guarantees independence of image size.& COPY; 2023 Elsevier Inc. All rights reserved.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] An image sensor based on optical Radon transform
    Bimber, Oliver
    COMPUTERS & GRAPHICS-UK, 2015, 53 : 37 - 43
  • [2] Rearranged Radon Transform Based Noise Robustness Image Retrieval
    An, Youngeun
    Kim, Gukjeong
    Ohl, Sangeon
    Chang, Minhyuk
    Park, Jongan
    2015 INTERNATIONAL CONFERENCE ON PLATFORM TECHNOLOGY AND SERVICE (PLATCON), 2015, : 21 - 22
  • [3] RADON TRANSFORM: IMAGE RECONSTRUCTION AND IDENTIFICATION OF NOISE AND INSTRUMENTAL ARTIFACTS
    D'Acunto, Mario
    Benassi, Antonio
    Moroni, Davide
    Salvetti, Ovidio
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 2280 - 2284
  • [4] An image similarity invariant feature extraction method based on radon transform
    Guo, Hongjun
    Chen, Lili
    International Journal of Circuits, Systems and Signal Processing, 2021, 15 : 288 - 296
  • [5] Three feature functions based on radon transform for image retrieval and object recognition
    Ma, Z. (zipingma@gmail.com), 1600, Academy Publisher (08):
  • [6] AN INTERESTING FEATURE OF THE RADON-TRANSFORM
    WEINSTEIN, FS
    APPLIED MATHEMATICS LETTERS, 1995, 8 (04) : 75 - 77
  • [7] Radon/ridgelet signature for image authentication
    Yao, Z
    Rajpoot, N
    ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5, 2004, : 43 - 46
  • [8] TARGET IMAGE DETECTION IN MARINE ENVIRONMENT BASED ON RADON TRANSFORM AND FEATURE SPACE DECISION
    Liu, Guangyu
    Xing, Chuanxi
    Shen, Zhengyan
    Huang, Yi
    Zhao, Enming
    FRESENIUS ENVIRONMENTAL BULLETIN, 2021, 30 (01): : 457 - 464
  • [9] Image and feature reconstruction for the attenuated Radon transform via circular harmonic decomposition of the kernel
    Rigaud, G.
    Lakhal, A.
    INVERSE PROBLEMS, 2015, 31 (02)
  • [10] A Radon-Transform-Based Image Noise Filter-With Applications to Multibeam Bathymetry
    Landmark, Knut
    Solberg, Anne H. Schistad
    Albregtsen, Fritz
    Austeng, Andreas
    Hansen, Roy Edgar
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (11): : 6252 - 6273