Shim Bo Seok, Hou Jong-Uk
Division of Software, Hallym University, Chuncheon 24252, Republic of Korea.
Sensors (Basel). 2023 Oct 5;23(19):8250. doi: 10.3390/s23198250.
This study emphasizes the significance of estimating the layer thickness and identifying slicer programs in the realm of 3D printing forensics. With the progress in 3D printing technology, precise estimation of the layer thickness has become crucial. However, previous research on layer thickness estimation has mainly treated the problem as a classification task, which is inadequate for continuous layer thickness parameters. Furthermore, previous studies have concentrated on hardware-based printer identification, but the identification of slicer programs through 3D objects is a vital aspect of the software domain and can provide valuable clues for 3D printing forensics. In this study, a regression-based approach utilizing a vision transformer model was proposed. Experiments conducted on the SI3DP++ dataset demonstrated that the proposed model could handle a broad range of data and outperform the current classification models. Additionally, this study proposed a new research direction by introducing slicer program identification, which significantly contributes to the field of 3D printing forensics.
本研究强调了在3D打印取证领域估计层厚和识别切片程序的重要性。随着3D打印技术的进步，精确估计层厚变得至关重要。然而，以往关于层厚估计的研究主要将该问题视为分类任务，这对于连续的层厚参数而言是不够的。此外，以往的研究集中在基于硬件的打印机识别上，但通过3D物体识别切片程序是软件领域的一个重要方面，可为3D打印取证提供有价值的线索。在本研究中，提出了一种利用视觉Transformer模型的基于回归的方法。在SI3DP++数据集上进行的实验表明，所提出的模型能够处理广泛的数据，并且优于当前的分类模型。此外，本研究通过引入切片程序识别提出了一个新的研究方向，这对3D打印取证领域有重大贡献。
