Compressive sensing based image compression-encryption using Novel 1D-Chaotic map

Compressive sensing based encryption achieves simultaneous compression-encryption by utilizing a low complex sampling process, which is computationally secure. In this paper, a new novel 1D–chaotic map is proposed that is used to construct an incoherence rotated chaotic measurement matrix. The chaotic property of the proposed map is experimentally analysed. The linear measurements obtained are confused and diffused using the chaotic sequence generated using the proposed map. The chaos based measurement matrix construction results in reduced data storage and bandwidth requirements. As it needs to store only the parameters required to generate the chaotic sequence. Also, the sensitivity of the chaos to the parameters makes the data transmission secure. The secret key used in the encryption process is dependent on both the input data and the parameter used to generate the chaotic map. Hence the proposed scheme can resist chosen plaintext attack. The key space of the proposed scheme is large enough to thwart statistical attacks. Experimental results and the security analysis verifies the security and effectiveness of the proposed compression-encryption scheme.

[1]  R. Amutha,et al.  Double image compression and encryption scheme using logistic mapped convolution and cellular automata , 2017, Multimedia Tools and Applications.

[2]  Hongbin Zha,et al.  Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[3]  Weiming Shen,et al.  Research of incoherence rotated chaotic measurement matrix in compressed sensing , 2017, Multimedia Tools and Applications.

[4]  David S. Rosenblum,et al.  From action to activity: Sensor-based activity recognition , 2016, Neurocomputing.

[5]  Wei Zhang,et al.  Image encryption based on three-dimensional bit matrix permutation , 2016, Signal Process..

[6]  Safya Belghith,et al.  A novel image encryption scheme based on substitution-permutation network and chaos , 2016, Signal Process..

[7]  Jiantao Zhou,et al.  A Review of Compressive Sensing in Information Security Field , 2016, IEEE Access.

[8]  Y. Asnath Victy Phamila,et al.  Low complexity energy efficient very low bit-rate image compression scheme for wireless sensor network , 2013, Inf. Process. Lett..

[9]  Zheng Qin,et al.  Data embedding in digital images using critical functions , 2017, Signal Process. Image Commun..

[10]  G. Sharma,et al.  On the security and robustness of encryption via compressed sensing , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[11]  Yicong Zhou,et al.  A new 1D chaotic system for image encryption , 2014, Signal Process..

[12]  Riccardo Rovatti,et al.  Submitted to Ieee Transactions on Signal Processing Low-complexity Multiclass Encryption by Compressed Sensing Part I: Definition and Main Properties , 2022 .

[13]  Kwok-Wo Wong,et al.  A chaos-based joint image compression and encryption scheme using DCT and SHA-1 , 2011, Appl. Soft Comput..

[14]  Luming Zhang,et al.  Fortune Teller: Predicting Your Career Path , 2016, AAAI.

[15]  E. Candès.  The restricted isometry property and its implications for compressed sensing , 2008 .

[16]  Entao Liu,et al.  Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing ∗ , 2010 .

[17]  Robin Fay,et al.  Introducing the counter mode of operation to Compressed Sensing based encryption , 2016, Inf. Process. Lett..

[18]  Dawei Wang,et al.  A novel lossless color image encryption scheme using 2 D DWT and 6 D hyperchaotic system , 2016 .

[19]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[20]  Jianhua Wu,et al.  Novel hybrid image compression–encryption algorithm based on compressive sensing , 2014 .

[21]  Seiichi Uchida,et al.  A parallel image encryption method based on compressive sensing , 2012, Multimedia Tools and Applications.

[22]  Yu Zheng,et al.  Urban Water Quality Prediction Based on Multi-Task Multi-View Learning , 2016, IJCAI.

[23]  Jian Weng,et al.  Enabling Secure and Fast Indexing for Privacy-Assured Healthcare Monitoring via Compressive Sensing , 2016, IEEE Transactions on Multimedia.

[24]  Yicong Zhou,et al.  Cascade Chaotic System With Applications , 2015, IEEE Transactions on Cybernetics.

[25]  Luming Zhang,et al.  Action2Activity: Recognizing Complex Activities from Sensor Data , 2015, IJCAI.

[26]  Li Liu,et al.  Recognizing Complex Activities by a Probabilistic Interval-Based Model , 2016, AAAI.

[27]  Hong Sun,et al.  Compressive Sensing With Chaotic Sequence , 2010, IEEE Signal Processing Letters.

[28]  Period Three Trajectories of the Logistic Map , 1996 .

[29]  Xin Liao,et al.  Reversible data hiding in encrypted images based on absolute mean difference of multiple neighboring pixels , 2015, J. Vis. Commun. Image Represent..

[30]  Xiaofei Wang,et al.  Human action recognition via compressive-sensing-based dimensionality reduction , 2015 .

[31]  Lyle H. Ungar,et al.  Beyond Binary Labels: Political Ideology Prediction of Twitter Users , 2017, ACL.

[32]  R. Amutha,et al.  A new Multistage multiple image encryption using a combination of Chaotic Block Cipher and Iterative Fractional Fourier Transform , 2014, 2014 First International Conference on Networks & Soft Computing (ICNSC2014).

[33]  Riccardo Rovatti,et al.  On Known-Plaintext Attacks to a Compressed Sensing-Based Encryption: A Quantitative Analysis , 2013, IEEE Transactions on Information Forensics and Security.

[34]  Mohammad Hossein Moattar,et al.  Color image encryption based on hybrid hyper-chaotic system and cellular automata , 2017 .

[35]  Hongbin Zha,et al.  Tracking Generic Human Motion via Fusion of Low- and High-Dimensional Approaches , 2013, IEEE Trans. Syst. Man Cybern. Syst..

[36]  Y. Rachlin,et al.  The secrecy of compressed sensing measurements , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[37]  Rong Huang,et al.  A Robust and Compression-Combined Digital Image Encryption Method Based on Compressive Sensing , 2011, 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[38]  Kwok-Wo Wong,et al.  Bi-level Protected Compressive Sampling , 2016, IEEE Transactions on Multimedia.

[39]  R. Amutha,et al.  Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks , 2015 .

[40]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[41]  Yong Wang,et al.  An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications , 2017, J. Vis. Commun. Image Represent..

[42]  Yicong Zhou,et al.  2D Sine Logistic modulation map for image encryption , 2015, Inf. Sci..

[43]  Xiangde Zhang,et al.  A novel image encryption-compression scheme using hyper-chaos and Chinese remainder theorem , 2013, Signal Process. Image Commun..

[44]  Hongbin Zha,et al.  Visual analysis of child-adult interactive behaviors in video sequences , 2010, 2010 16th International Conference on Virtual Systems and Multimedia.

[45]  Xin Liao,et al.  Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform , 2017, Multimedia Tools and Applications.

[46]  Dustin G. Mixon,et al.  Certifying the Restricted Isometry Property is Hard , 2012, IEEE Transactions on Information Theory.

[47]  Li-Hua Gong,et al.  Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing , 2014 .

[48]  X. Tong,et al.  A new algorithm of the combination of image compression and encryption technology based on cross chaotic map , 2013 .