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.

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.