B. S. Shim, J. H. Choe and J. -U. Hou, "Source Identification of 3D Printer Based on Layered Texture Encoders," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3233764.
Dataset will be available after publication. Please refer our previous work SI3DP
With the rapid growth in the 3D printing content market, various unprecedented criminal cases and copyright protection issues are emerging. In response to this imminent and emergent difficulty, we propose a forensic technique for identifying the source of 3D printed products based only on the surface image with no additional tools. This is used to develop database and benchmarks to be used for tracing and finding the sources and origins of 3D printing objects. In fact, the surface texture of 3D printed objects presents, inevitably, very fine periodic traces owing to hardware characteristics during the manufacturing process. We systematically analyzed the causes and factors related to these fine features, and propose a technology using them as unique fingerprints for specific 3D printed objects. As benchmarks, we obtained microscopic close-up images and full-object images from 464 printed objects from 29 different printing options. A certain level of performance was achieved using six benchmarks including printer, and device-level identification. We improved the classification performance to an accuracy of 95% by using additional inspection information. Moreover, we extended the baseline study based on the benchmark set to forensic test scenarios from multiple perspectives in preparation for real situations. Finally, we deformed 3D printed objects and applied our approach to validate post-processing situations to avoid source identification. We reveal both the dataset and detailed experimental design to provide an opportunity to facilitate future in-depth research related to forensics and protection of intellectual property.
- Python 3.7.0 ~ 3.7.9
- CUDA Version 11.0
-
Nvidia driver, CUDA toolkit 11.0, install Anaconda.
-
Install pytorch
conda install pytorch torchvision cudatoolkit=11.0 -c pytorch -
Install various necessary packages in requirements.txt
pip install -r requirements.txt
Sign up through the link below to receive Dataset and csv.
Download 7zip and unzip your dataset. Create a data folder with SI3DPpp folder as a sub-path.
Please move the full shot and close-up dataset folders in the SI3DP folder.
Based on the ./data sub-path SI3DPpp/{train_close,train_full,train_close.csv,train_full.csv}
When using Terminal, directly execute the code below after setting the path
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close
When using pycharm:
Menu Run
-> Edit Configuration
-> Check train.py in Script path
-> Go to parameters and enter the following
--enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close
-> Running/debugging after check Apply button
As training progresses, the best and final weights are saved in the folder ./weights/. Learning logs are saved in the ./logs/ folder.
The order of side task type and task type is P : Printer, F : Filament, Q-T : Layer thickness, Q-S : Number of shells, R : Reprint.
-
Printer task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close -
Filament task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type F --img-type close -
Layer thickness task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type Q-T --img-type close -
Number of shells task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type Q-S --img-type close -
Device task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type D --img-type close -
Reprint task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type R --img-type close
-
Multi-Task(Device & Printer)
python train.py --enet-type CFTNet --task-type D --img-type close --side-task-type P --batch-size 32 --n-epochs 20 -
Multi-Task(Device & Layer thickness)
python train.py --enet-type CFTNet --task-type D --img-type close --side-task-type Q-T --batch-size 32 --n-epochs 20
-Set according to the combination of "--task-type" and "--side-task-type".
-
Single-Modal-Task(Device)
python train.py --enet-type CFTNet --task-type D --img-type both --batch-size 32 --n-epochs 20 -
Multi-Modal-Task(Device & Printer)
python train.py --enet-type CFTNet --task-type D --img-type both --side-task-type P --batch-size 32 --epoch 20 -
Multi-Modal-Task(Device & Printer & Inspection data)
python train.py --enet-type CFTNet --task-type D --img-type both --side-task-type P --batch-size 32 --epoch 20 --use-meta
-
Multi-Modal-Task(Printer & Number of shells)
python train.py --enet-type CFTNet --task-type P --img-type both --side-task-type Q-S --batch-size 32 --epoch 20 --semi -
Multi-Modal-Task(Device & Layer thickness)
python train.py --enet-type CFTNet --task-type D --img-type both --side-task-type Q-T --batch-size 32 --epoch 20 --semi
-
Sanding-Processing-Task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close --sanding-processing -
Coating-Processing-Task
python train.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close --coating-processing
-
Printer/Filament/Layer thickness/Number of shells/Device/Reprint task
python train.py --enet-type tf_efficientnet_b3_ns --n-epochs 20 --batch-size 32 --task-type P/F/Q-T/Q-S/D/R --img-type full --baseline
The learned model is subjected to k-fold cross validation. You can use the model used for training earlier, or you can evaluate it by specifying the model in --model-dir.
The evaluation results are saved in ./logs/.
-
Printer/Filament/Layer thickness/Number of shells/Device/Reprint task
python evaluate.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P/F/Q-T/Q-S/D/R --img-type close
-
Multi-Task(Device & Printer)
python evaluate.py --enet-type CFTNet --task-type D --img-type close --side-task-type P --batch-size 32 --n-epochs 20 -
Multi-Task(Device & Layer thickness)
python evaluate.py --enet-type CFTNet --task-type D --img-type close --side-task-type Q-T --batch-size 32 --n-epochs 20
-Set according to the combination of "--task-type" and "--side-task-type".
-
Single-Modal-Task(Device)
python evaluate.py --enet-type CFTNet --task-type D --img-type both --batch-size 32 --n-epochs 20 -
Multi-Modal-Task(Device & Printer)
python evaluate.py --enet-type CFTNet --task-type D --img-type both --side-task-type P --batch-size 32 --epoch 20 -
Multi-Modal-Task(Device & Printer & Inspection data)
python evaluate.py --enet-type CFTNet --task-type D --img-type both --side-task-type P --batch-size 32 --epoch 20 --use-meta
-
Multi-Modal-Task(Printer & Number of shells)
python evaluate.py --enet-type CFTNet --task-type P --img-type both --side-task-type Q-S --batch-size 32 --epoch 20 --semi -
Multi-Modal-Task(Device & Layer thickness)
python evaluate.py --enet-type CFTNet --task-type D --img-type both --side-task-type Q-T --batch-size 32 --epoch 20 --semi
-
Sanding-Processing-Task
python evaluate.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close --sanding-processing -
Coating-Processing-Task
python evaluate.py --enet-type CFTNet --n-epochs 20 --batch-size 32 --task-type P --img-type close --coating-processing
-
Printer/Filament/Layer thickness/Number of shells/Device/Reprint task
python evaluate.py --enet-type tf_efficientnet_b3_ns --n-epochs 20 --batch-size 32 --task-type P/F/Q-T/Q-S/D/R --img-type full --baseline
If you want to cite our Datasets paper and code, you can use a bibtex code here:
@article{shim2023source,
title={Source Identification of 3D Printer Based on Layered Texture Encoders},
author={Shim, Bo Seok and Choe, Jae Hong and Hou, Jong-Uk},
journal={IEEE Transactions on Multimedia},
year={2023},
publisher={IEEE}
}