Block-wise Scrambled Image Recognition Using Adaptation Network

In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines. Hence, both the perceptual information hiding and the corresponding object recognition methods should be developed. Block-wise image scrambling is introduced to hide perceptual information from a third party. In addition, an adaptation network is proposed to recognize those scrambled images. Experimental comparisons conducted using CIFAR datasets demonstrated that the proposed adaptation network performed well in incorporating simple perceptual information hiding into DNN-based image classification.

[1]  Mauro Barni,et al.  Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty computation , 2013, IEEE Signal Processing Magazine.

[2]  Jack Xin,et al.  AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks , 2020, KDD.

[3]  Jack Xin,et al.  Minimization of ℓ1-2 for Compressed Sensing , 2015, SIAM J. Sci. Comput..

[4]  Jun Sakuma,et al.  Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data , 2016, NDSS.

[5]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[6]  Shie Mannor,et al.  The cross entropy method for classification , 2005, ICML.

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  Masayuki Tanaka,et al.  Learnable Image Encryption , 2018, 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[9]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Jack Xin,et al.  A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications , 2013, SIAM J. Imaging Sci..

[11]  Marshall Copeland,et al.  Microsoft Azure , 2015, Apress.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Hitoshi Kiya,et al.  Encryption-Then-Compression Systems Using Grayscale-Based Image Encryption for JPEG Images , 2018, IEEE Transactions on Information Forensics and Security.

[14]  Takuya Akiba,et al.  Shakedrop Regularization for Deep Residual Learning , 2018, IEEE Access.

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).