论文标题
使用适应网络的障碍物炒图像识别
Block-wise Scrambled Image Recognition Using Adaptation Network
论文作者
论文摘要
在这项研究中，研究了一种具有感知隐藏的对象识别方法，以生成人类可识别的安全图像，而不是机器可识别的。因此，应开发感知信息隐藏和相应的对象识别方法。介绍了块图像，以从第三方隐藏感知信息。此外，提出了一个适应网络以识别那些炒图像。使用CIFAR数据集进行的实验比较表明，所提出的适应网络在将隐藏在基于DNN的图像分类中的简单感知信息中表现出色。
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.
