Although several watermarking techniques have been proposed for spherical panoramic content, most have focused on simple leakage situations and have not addressed the various copyright leakage scenarios specific to spherical panoramic content. Such leakage scenarios are yet to be thoroughly analyzed in the literature. Diverse scenarios can occur in the case of spherical panoramic content depending on the rendering process and stage of image leakage. A distinct watermarking method is required for each scenario. In this study, six leakage scenarios for spherical panoramic content were identified, and the requirements for effective watermarking methods were examined. Without the original source information, existing watermarking techniques generally fail to protect copyrights. To this end, we propose two supplementary methods to enhance blind watermarking techniques. In the first method, a deep learning model designed for steganalysis was used to detect vertical viewpoints from perspective images without using the original source image. In the second method, a template was used to increase the robustness against spherical angle translation attacks. Using these two supplementary methods, we achieved comprehensive coverage across all scenarios that utilize existing watermarking techniques.

This study systematically addresses the issues of copyright infringement that have emerged with the introduction of neural radiance fields (NeRFs) by defining scenarios and applying and analyzing existing protective technologies tailored to each case. To formalize these threats, we introduce a threat model consisting of security requirements against modality shifts and an attacker model that classifies misuse based on the modalities held by the creator and attacker. This results in nine representative infringement cases that guide our experimental design. Our novel end-to-end watermarking approach is robust against modality changes caused by neural rendering. Independent decoders that vary according to the domain are proposed to extract messages from watermarked data effectively. In addition, a generalized neural rendering (GNR) module is utilized to optimize the various 3D models. These elements are integrated in an end-to-end manner, enhancing the interaction between different modalities and improving the overall performance. This study systematically examines the effectiveness of both the existing and proposed methods through experiments across different distribution modalities.

With the rapid growth in the three-dimensional (3D) printing content market, various unprecedented criminal cases and copyright protection issues have emerged. In response to this imminent and emergent difficulty, we propose a forensic technique for identifying the source of 3D printed products based only on surface inspection features. The surface texture of 3D printed objects exhibits, inevitably, extremely fine periodic features during the additive manufacturing process. We propose a two-stream texture encoder, referred to as CFTNet, combined with fast Fourier transform and positional encoding of the transformer encoder to leverage inherent periodic features occurring during the additive manufacturing. As benchmarks, we define detailed scenarios for six source identification problems and present detailed verification procedures with a large-scale benchmark dataset SI3DP++ for forensic real-world scenarios. A certain level of performance was achieved using six benchmarks, including printer and device-level identification. Moreover, we extended the baseline study based on the benchmark set to forensic test scenarios from multiple perspectives in preparation for real situations. We reveal both the dataset and detailed experimental design to provide an opportunity to facilitate future in-depth studies related to forensics and protection of intellectual property.

The blind estimation of image geometric transformation is an essential problem in digital image forensics. In this paper, we propose an end-to-end transformer-based estimator that can predict the geometric transformation parameters of an image. Deviating from the existing classification-based formulation, we provided a more generalized method by directly estimating the transformation matrix. We note that the frequency peak position of the inherent resampling artifacts leaves explicit clues for the geometric transformation. To use this feature, a direct analysis of the spatial frequency is performed using the positional encoding of fast Fourier transform and multi-head self-attention. Combining the regression layers with the preceding transformer effectively analyzes the geometric transformation parameters of the image. Performing extensive comparison tests with a public database, the proposed method demonstrates a prediction performance higher than existing methods and also demonstrated robustness to JPEG compression.

This paper lays the foundation for a new 3D content market by establishing a content security framework using databases and benchmarks for in-depth research on source identification of 3D printed objects. The proposed benchmark, SI3DP dataset, offers a more generalized multimedia forensic technique. Assuming that identifying the source of a 3D printing object can arise from various invisible traces occurring in the printing process, we obtain close-up images, full object images from 252 printed objects from 18 different printing setups. We then propose a benchmark with five challenging tasks such as device-level identification and scan-and-reprint detection using the provided dataset. Our baseline shows that the printer type and its attributes can be identified based on the microscopic difference of surface texture. Contrary to the conventional belief that only microscopic views such as close-up images are useful to identify printer model, we also achieved a certain level of performance even at a relatively macroscopic point of view. We then propose a multitask-multimodal architecture for device-level identification task to exploit rich knowledge from different image modality and task. The SI3DP dataset can promote future in-depth research studies related to digital forensics and intellectual property protection.

After the works on the state-of-the-art High Efficiency Video Coding (HEVC) standard, the standard organizations continued to study the potential video coding technologies for the next generation of video coding standard, named Versatile Video Coding (VVC). Transform is a key technique for compression efficiency, and core experiment 6 (CE6) is carried out to explore the transform related coding tools. In this paper, we propose a novel separable transform based on Karhunen-Loève Transform (KLT) to eliminate the horizontal and vertical correlations in the residual samples of intra coding. In the proposed method, the weaknesses of the traditional KLT are addressed. The separable KLT is developed as an alternative transform type in addition to DCT-II, and the transform matrices from 4×4 to 64×64 are trained from intra residual samples. Experimental results show the proposed method can achieve 2.7% bitrate saving averagely on top of the reference software of VVC (VTM-1.1), and the consistent performance improvement on test set also validates the strong generalization capacity of the proposed separable KLT.

Nowadays, three-dimensional (3D) point cloud has been an emerging medium to represent real-world scenes and objects. However, there is a considerable proportion of point clouds whose color attribute information is not captured during the acquisition process due to the device or environment limitations. This poses a great challenge for efficient management and utilization of point clouds. To address this problem, we introduce an automatic colorization scheme based on a deep generative network for 3D point clouds. The proposed approach uses the range images of point could geometry and trains a conditional generative adversarial network to predict the color of those images. Later, the color of each pixel in the colorized image is projected back to its corresponding point in the 3D point cloud. The experimental results demonstrate the efficacy of the proposed colorization approach in facilitating users to recognize and handle 3D point cloud data better.

As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image.

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.

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.

We propose a new protection scheme for displaying a static binary image on a screen. The protection is achieved by a visual cryptography algorithm that divides the target images into several divisions. The visual difference between the text and the background is induced by exploiting the temporal responsibilities of the human eye. With the results of our user study, we demonstrate that encrypted visual information was mentally recovered by the human visual system. Moreover, the images captured from our scheme do not provide any meaningful information to the human eye, so that our method provides a strong security measure against screenshot piracy.

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.

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.