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.