Secure Representation of Images Using Multi-layer Compression

被引:2
|
作者
Ferdowsi, Sohrab [1 ]
Voloshynovskiy, Sviatoslav [1 ]
Kostadinov, Dimche [1 ]
Korytkowski, Marcin [2 ]
Scherer, Rafal [2 ]
机构
[1] Univ Geneva, Dept Comp Sci, CH-1227 Carouge, Switzerland
[2] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland
关键词
Image compression; Image scrambling; Dictionary learning; Rate-distortion theory; Privacy preservation;
D O I
10.1007/978-3-319-19324-3_62
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We analyze the privacy preservation capabilities of a previously introduced multi-stage image representation framework where blocks of images with similar statistics are decomposed into different codebooks (dictionaries). There it was shown that at very low rate regimes, the method is capable of compressing images that come from the same family with results superior to those of the JPEG2000 codec. We consider two different elements to be added to the discussed approach to achieve a joint compression-encryption framework. The first visual scrambling is the random projections were the random matrix is kept secret between the encryption and decryption sides. We show that for the second approach, scrambling in the DCT domain, we can even slightly increase the compression performance of the multi-layer approach while making it safe against de-scrambling attacks. The experiments were carried out on the ExtendedYaleB database of facial images.
引用
收藏
页码:696 / 705
页数:10
相关论文
共 50 条
  • [1] Compression of encrypted images with multi-layer decomposition
    Xinpeng Zhang
    Guangling Sun
    Liquan Shen
    Chuan Qin
    Multimedia Tools and Applications, 2014, 72 : 489 - 502
  • [2] Compression of encrypted images with multi-layer decomposition
    Zhang, Xinpeng
    Sun, Guangling
    Shen, Liquan
    Qin, Chuan
    MULTIMEDIA TOOLS AND APPLICATIONS, 2014, 72 (01) : 489 - 502
  • [3] A MULTI-LAYER IMAGE REPRESENTATION USING REGULARIZED RESIDUAL QUANTIZATION: APPLICATION TO COMPRESSION AND DENOISING
    Ferdowsi, Sohrab
    Voloshynovskiy, Slava
    Kostadinov, Dimche
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017, : 2697 - 2701
  • [4] Image compression using multi-layer neural networks
    AbdelWahhab, O
    Fahmy, MM
    SECOND IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS, PROCEEDINGS, 1997, : 179 - 183
  • [5] Fusion of infrared and visible images via multi-layer convolutional sparse representation
    Zhang, Zhouyu
    He, Chenyuan
    Wang, Hai
    Cai, Yingfeng
    Chen, Long
    Gan, Zhihua
    Huang, Fenghua
    Zhang, Yiqun
    JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2024, 36 (06)
  • [6] Symbolic representation of a multi-layer perceptron
    Mouria-Beji, F
    ARTIFICIAL NEURAL NETS AND GENETIC ALGORITHMS, 2001, : 205 - 208
  • [7] On the representation of multi-layer woven structure
    Yi, Honglei
    Ding, Xin
    Journal of Dong Hua University (English Edition), 1999, 16 (03): : 14 - 16
  • [8] Stereo Magnification with Multi-Layer Images
    Khakhulin, T.
    Korzhenkov, D.
    Solovev, P.
    Sterkin, G.
    Ardelean, A-T
    Lempitsky, V
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2022, : 8677 - 8686
  • [9] Multi-layer Representation Learning for Medical Concepts
    Choi, Edward
    Bahadori, Mohammad Taha
    Searles, Elizabeth
    Coffey, Catherine
    Thompson, Michael
    Bost, James
    Tejedor-Sojo, Javier
    Sun, Jimeng
    KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 1495 - 1504
  • [10] Multi-layer Filtering Approach for Map Images
    Chen, Minjie
    Xu, Mantao
    Fraenti, Pasi
    2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, : 3953 - 3956