Recently, with most mobile phones coming with dual cameras, stereo image super-resolution is becoming increasingly popular in phones and other modern acquisition devices, leading stereo super-resolution images spread widely on the Internet. However, current image forensics methods are carried out in monocular images, and high false positive rate appears when detecting stereo super-resolution images by these methods. Therefore, it is important to develop stereo super-resolution image detection method. In this paper, a convolutional neural network with multi-scale feature extraction and hierarchical feature fusion is proposed to detect the stereo super-resolution images. Multi-atrous convolutions are employed to extract multi-scale features and be adapt for varying stereo super-resolution images, and hierarchical feature fusion further improve the performance and robustness of the model. Experimental results demonstrate that the proposed network can detect stereo super-resolution images effectively and achieve strong generalization and robustness. To the best of our knowledge, it is the first attempt to investigate the performance of current forensics methods when tested under stereo super-resolution images, and represent the first study of stereo super-resolution images detection. We believe that it can raise the awareness about the security of stereo super-resolution images.

近来，随着大部分手机都配备双摄像头，立体图像超分辨率在手机等现代采集设备中越来越流行，导致立体超分辨率图像在互联网上广泛传播。然而，目前的图像取证方法都是针对单目图像进行的，这些方法在检测立体超分辨率图像时会出现较高的误报率。因此，发展立体超分辨率图像检测方法具有重要意义。在本文中，提出了一种具有多尺度特征提取和分层特征融合的卷积神经网络来检测立体超分辨率图像。多孔卷积用于提取多尺度特征并适应不同的立体超分辨率图像，和分层特征融合进一步提高了模型的性能和鲁棒性。实验结果表明，所提出的网络可以有效地检测立体超分辨率图像，并具有很强的泛化性和鲁棒性。据我们所知，这是首次尝试在立体超分辨率图像下测试当前取证方法的性能，并且代表了对立体超分辨率图像检测的首次研究。我们相信它可以提高人们对立体超分辨率图像安全性的认识。这是在立体超分辨率图像下测试时研究当前取证方法性能的第一次尝试，代表了立体超分辨率图像检测的第一次研究。我们相信它可以提高人们对立体超分辨率图像安全性的认识。这是在立体超分辨率图像下测试时研究当前取证方法性能的第一次尝试，代表了立体超分辨率图像检测的第一次研究。我们相信它可以提高人们对立体超分辨率图像安全性的认识。