基于对抗样本的深度学习图像压缩感知方法

压缩感知是研究数据采样压缩与重构的信号处理新理论，近年来研究人员将深度学习运用到图像压缩感知算法中，显著提高了图像重构质量.然而，图像信息常与隐私关联，高质量的重构图像在方便人们观赏的同时，带来了隐私保护的问题.本文基于深度学习理论，提出一种对抗的图像压缩感知方法.该方法将压缩理论和对抗样本技术统一于同一个压缩感知算法，通过设计损失函数，联合重构误差和分类误差来训练压缩感知深度神经网络，使得压缩感知重构样本同时也是一个对抗样本.因此，重构图像在保证重构质量的同时，也能对抗图像分类算法，降低其识别率，达到保护图像隐私的效果.在Cifar-10和MNIST图像集上进行的实验结果表明，和已有的压缩感知方法相比，我们提出的对抗压缩感知方法以损失仅10%的图像重构质量为代价，使得图像分类精度下降了74%，获得了很好的对抗性能.

Method of Deep Learning Image Compressed Sensing Based on Adversarial Samples

Compressed sensing is a new signal processing theory focusing on data sampling compression and reconstruction. In recent years, researchers have applied deep learning to image compressed sensing algorithms, which significantly improves the quality of the recovered images. However, images are often associated with personal privacy, and high-quality recovered images often bring privacy protection problems while facilitating people''s viewing. Based on deep neural network, this paper proposes an image compressed sensing algorithm with adversarial learning. This method integrates data compression and adversary sample technique into the compressed sensing algorithm. By training the neural network with a loss function combining reconstruction loss and classification loss, the output samples, i.e., the recovered images, become adversarial samples. The recovered images with our proposed algorithm can then be adversarial to image classifications algorithms, decreasing their recognition rate and achieving the performance of protecting image privacy while guaranteeing a reasonable image quality. Experimental results on Cifar-10 and MNIST show that, compared with the existing compressed sensing methods, the proposed adversarial algorithm achieves excellent adversarial performance, as the classification accuracy is decreased by 74% at the cost of 10% loss of image reconstruction quality.