Jong-Uk Hou

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.

Blind 3D Mesh Watermarking for 3D Printed Model by Analyzing Layering Artifact

Because they will impact so many areas, copyright issues will inevitably arise as 3D printing expands into the content industry. The problem is that protections based on conventional methods are not effective, because the 3D printing process disables those protections. In this paper, we propose a robust and blind watermarking scheme that is able to protect content not only when the 3D model is shared in the digital world, but also when the 3D digital content is converted to analog content by 3D printing. First, we base our proposed watermark on a component that is unchanging to the printing direction for robustness against the printing process. The printing artifacts, instead of being regarded as severe distortion, are treated as a template that provides orientation information to the watermark detector. To achieve this, we also propose a blind estimation algorithm for the printing direction that starts from an analysis of the layering artifact. Using the results from a proposed estimator, the watermark from the printed-and-scanned model is synchronized with the original orientation. With the results of our tests with various 3D mesh models and attacks, we experimentally verified that the proposed method does not lose embedded patterns during the 3D print-scan process, especially with low-cost printers. Moreover, our method provides a new solution for estimating the printing direction that will be useful in a variety of fields.

Detection of Hue Modification Using Photo Response Nonuniformity

Hue modification is a common strategy used to distort the true meaning of a digital image. In order to detect this kind of image forgery, we proposed a robust forensics scheme for detecting hue modification. First, we pointed out that photo response nonuniformity (PRNU) separated by a color filter array forms a pattern independent of others, since the position of each PRNU pixel means that they do not overlap. Using PRNUs from each color channel of an image, we designed a forensic scheme for estimating hue modification. We also proposed an efficient estimation scheme and an algorithm for detecting partial manipulation. The results confirmed that the proposed method distinguishes hue modification and estimates the degree of change; moreover, it is resistant to the effects of common image processing.

Robust video watermarking for MPEG compression and DA-AD conversion

In this paper, we proposed a robust video watermarking system. Using DCT domain spread-spectrum watermarking, our system achieved robustness to various non-hostile video processings such as MPEG compression, and DA-AD conversion. Our proposed system guarantees that 16-bit data can be extracted in any 15 second interval, even in a long video, by using adaptive frame selection. To verify the performance of the proposed system, various experiments were carried out under a DA-AD conversion environment. Experimental results show that the proposed method is not only robust to non-hostile video processings but also achieves invisibility.

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.

Hue modification estimation using sensor pattern noise

In digital image forensics, previous methods for hue forgery detection cannot be used after common image processing such as resizing and JPEG compression. In this paper, we suggest a robust forensics scheme for estimating hue modification of images. To achieve this goal, we use sensor pattern noise from each color channel of un-tampered images as the ground truth. Since we know the unique characteristics of each color channel, we can estimate a hue modification by testing suspicious images for all hue changes. The results confirms that the proposed method distinguishes hue modification and estimates the changed degree; moreover, it provides robustness against resizing and JPEG compression.

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.

Exposing Digital Forgeries by Detecting a Contextual Violation Using Deep Neural Networks

Previous digital image forensics focused on the low-level features that include traces of the image modifying history. In this paper, we present a framework to detect the manipulation of images through a contextual violation. First, we proposed a context learning convolutional neural networks (CL-CNN) that detects the contextual violation in the image. In combination with a well-known object detector such as R-CNN, the proposed method can evaluate the contextual scores according to the combination of objects in the image. Through experiments, we showed that our method effectively detects the contextual violation in the target image.

Robust 3D Mesh Watermarking Scheme for an Anti-Collusion Fingerprint Code

Collusion attack is one of the techniques used for unauthorized removal of embedded marks. In this paper, we propose a robust 3D mesh fingerprinting scheme for an anti-collusion code. In contrast to the existing robust mesh watermarking which provides unsuitable primitives for anti-collusion code, the proposed method has well-operated capacity to carry the anti-collusion fingerprint code. In order to minimize the detection error, we also modeled the response of the detector and herein present optimized thresholds for our method. Based on the experiments, the proposed method outperformed conventional robust mesh watermarking against collusion attack in all cases.

Detecting digital image forgery in near-infrared image of CCTV

The reliability of CCTV digital images is more important than the reliability of many other types of images. However, image editing tools such as Photoshop make this unreliable. CCTV uses two photography modes, the RGB mode and the near-infrared mode. While near-infrared images have different properties, such as a constant level of source light intensity, and a constant direction of the source light, there are no forensic techniques for near-infrared images. In this paper, we propose a forensic technique based on a constant direction of the source light. In order to expose splicing forgery in near-infrared images, we create an ideal near-infrared image model of a plane. We then calculate gradient vectors of the model and objects in images. Depending on the similarity of two vectors, the image is determined forged or not. This forensic technique helps to improve the reliability of near-infrared images.