Sunghee Choi

Stereoscopic watermarking by horizontal noise mean shifting

Depth-image-based rendering (DIBR) is a method to represent a stereoscopic content. The DIBR consists of a monoscopic center view and an associated per-pixel depth map. Using these two components and given depth condition from a user, the DIBR renders left and right views. The advantages of DIBR are numerous. The user can choose not only the monoscopic or stereoscopic view selectively, but also the depth condition what he prefers when he watches a stereoscopic content. However, in the view of copyright protection, since not only the center view but also each left or right view can be used as a monoscopic content when they are illegally distributed, the watermark signal which is embedded in the center view must have an ability to protect the respective three views. In this study, we solve this problem by exploiting the horizontal noise mean shifting (HNMS) technique. We exploit the fact that the objects in the view are shifted only to horizontal way when the center view renders to the left and right views. Using this fact, the proposed stereoscopic watermarking scheme moves the mean of horizontal noise histogram which is invariant to horizontal shifting, and we achieve good performance as shown in the experimental results.

3D Print-Scan Resilient Watermarking Using a Histogram-Based Circular Shift Coding Structure

3D printing content is a new form of content being distributed in digital as well as analog domains. Therefore, its security is the biggest technical challenge of the content distribution service. In this paper, we analyze the 3D print-scan process, and we organize possible distortions according to the processes with respect to 3D mesh watermarking. Based on the analysis, we propose a circular shift coding structure for the 3D model. When the rotating disks of the coding structure are aligned in parallel to the layers of the 3D printing, the structure preserves a statistical feature of each disk from the layer dividing process. Based on the circular shift coding structure, we achieve a 3D print-scan resilient watermarking scheme. In experimental tests, the proposed scheme is robust against such signal processing, and cropping attacks. Furthermore, the embedded information is not lost after 3D print-scan process.

A robust 3D mesh watermarking scheme against cropping

Various watermarking schemes achieved the robustness against the usual operations such as simplification, remeshing and noise addition. However, the techniques were not robust against cropping, nevertheless the cropping attack is commonly performed by general editing. In this paper, we propose a robust 3D mesh watermarking method against cropping. We achieve the blind watermarking scheme involving consistent segmentation and the scheme improves the robustness against cropping. The experimental results show that the proposed method achieves higher performance than the previous 3D mesh watermarking methods.

Cropping-resilient 3D mesh watermarking based on consistent segmentation and mesh steganalysis

This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated by computing the consistency interval of vertex norms. In the watermark embedding process, the mesh is divided into several segments and the same watermark is inserted in each segment. In the watermark extraction process, the final watermark among watermark candidates extracted from multiple segments is determined through watermark trace analysis that is kind of mesh steganalysis. We analyze the watermark trace energy of multiple segments of a mesh and detect the final watermark in the segment with the highest watermark trace energy. To analyze the watermark trace energy, we employ nonlinear least-squares fitting. The experimental results show that the proposed method not only achieves significantly high robustness against cropping attack, but also resists common signal processing attacks such as additive noise, quantization, smoothing and simplification.

DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.

Orientation selection for printing 3D models

With the rapid maturing of 3D printing technologies, research on optimizing models to be printed in geometrical perspective has been considered; for example, model hollowing, partitioning. Much of these works however do not deal with the basic property that has to be determined in every 3D printing moment: model orientations. In this paper, we propose a novel approach to find reasonable orientations for fabricating arbitrary shaped 3D models with conventional layer-by-layer 3D printers. We observe that model orientations can affect some major factors representing the quality of 3D printing. We formulate a number of desirable criteria for the orientation, including material use, resolution error, and support remainder visibility. By optimizing these criteria, our proposed method automatically generates candidate orientations that produce cost-effective and visually pleasing printing results.

A SIFT features based blind watermarking for DIBR 3D images

Depth image based rendering (DIBR) is a promising technique for extending viewpoints with a monoscopic center image and its associated per-pixel depth map. With its numerous advantages including low-cost bandwidth, 2D-to-3D compatibility and adjustment of depth condition, DIBR has received much attention in the 3D research community. In the case of a DIBR-based broadcasting system, a malicious adversary can illegally distribute both a center view and synthesized virtual views as 2D and 3D content, respectively. To deal with the issue of copyright protection for DIBR 3D Images, we propose a scale invariant feature transform (SIFT) features based blind watermarking algorithm. To design the proposed method robust against synchronization attacks from DIBR operation, we exploited the parameters of the SIFT features: the location, scale and orientation. Because the DIBR operation is a type of translation transform, the proposed method uses high similarity between the SIFT parameters extracted from a synthesized virtual view and center view images. To enhance the capacity and security, we propose an orientation of keypoints based watermark pattern selection method. In addition, we use the spread spectrum technique for watermark embedding and perceptual masking taking into consideration the imperceptibility. Finally, the effectiveness of the presented method was experimentally verified by comparing with other previous schemes. The experimental results show that the proposed method is robust against synchronization attacks from DIBR operation. Furthermore, the proposed method is robust against signal distortions and typical attacks from geometric distortions such as translation and cropping.

Detecting composite image manipulation based on deep neural networks

In this paper, we propose a composite manipulation detection method based on convolutional neural networks (CNNs). To our best knowledge, this is the first work applying deep learning for composite forgery detection. The proposed technique defines three types of attacks that occurred frequently during image forging and detects when they are concurrently applied to images. To do this, we learn the statistical change due to the manipulation through the proposed CNN architecture and classify the manipulated image. The proposed technique is effective since it learns integrated image of composite manipulation and extracts characteristic distinguished from original image. Since most attacks are applied in a composite way in real environment, the approach of the proposed technique has practical advantages compared to traditional forensics scheme. In addition, the experimental results demonstrate the reliability of the proposed method through results of high performance.

Perceptual 3D Watermarking Using Mesh Saliency

In this paper, we introduce a novel blind 3D mesh watermarking method which focuses on preserving the appearance of the watermarked model. Despite the high transparency achieved by existing 3D watermarking schemes, we observe that only a small amount of geometric error can bring a significant impact to appearance of 3D models, especially in visually important regions. We integrate this human perceptual importance, called saliency, to control the distortions on surfaces. Our method enhances the imperceptibility while maintaining the efficiency of processing spatial information by conjugating spatial and spectral regions. We use the vertex norm distribution and solve the quadratic error minimization problem to insert watermark bits. Experimental results demonstrate that our method performs well for perceived visual quality and also robustness against various geometric attacks.