Face morphing attack has become a severe threat to the current face recognition systems. Though there are some methods for detecting face morphing, the performance of these methods is susceptible to noise. Aiming to enhance the performance of resisting noise in face morphing detection, a noise robust convolutional neural network is proposed in this paper. The structure of the network is divided into two parts: facial image adaptive denoising and face morphing detection. Before the face morphing detection, the auto-encoders are first utilized to adaptively denoise the noised facial images, which can effectively reduce the influence of noise on face morphing detection. Then, the pre-trained VGG19 convolution neural network with powerful classification ability is used for face morphing detection with the generated noise-free facial images. Experimental results indicate that the proposed method can effectively reduce the noise influence on face morphing detection, and can achieve better performance compared with some existing methods.
Aim to authenticate the integrity of 2D CAD engineering graphics, two reversible watermarking schemes based on asymmetric histogram shifting and complementary embedding are proposed. On the basis of coordinates and phases data correlation, multiple prediction errors are obtained to construct asymmetric histograms. Watermark is embedded by modifying the asymmetric histograms in opposite directions with dual complementary embedding strategy. Experimental results indicate that the proposed method outperforms prior works not only in capacity, but also in imperceptibility.
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