## 4 Experiments
### Material Classification  
We demonstrated that materials can be distinguished by analyzing their reflection properties in the SWIR range through image processing techniques. Beyond this approach, using deep learning to learn material-specific reflection differences could enable material  
Figure 11: Visible- (**a**) and SWIR (**b**)-range fruits/vegetables dataset examples.  
Figure 12: Eight visible and SWIR-range band image examples of the Human vs. Others Dataset. The figures shown in (**a–d**) correspond to examples of a human, a cotton mannequin, a plastic mannequin, and a banner object. Figures (**e,f**) depict test images featuring all objects together in a single scene.  
classification that standard RGB cameras, which capture only visible spectrum data, cannot perform. To validate this, we conducted a classification experiment using our fruits and vegetables dataset with ResNet-18 as our model. While more advanced models are now available, we opted for this earlier CNN-based model to show that even a previous-generation model can effectively learn these features. ResNet's residual learning framework mitigates performance degradation as network depth increases, achieving a lightweight yet effective structure with 18 layers. We fine-tuned a pre-trained ResNet-18 using our collected data.  
Figure 13a-d presents confusion matrices for each class using RGB data. There were several misclassifications: for instance, bananas and carrots were often classified as real when they were fake, while oranges and sweet peppers were sometimes incorrectly classified as fake when they were real. Though distinguishing real from fake is challenging for humans with visible-spectrum images, deep learning showed some ability to discern subtle appearance differences, achieving partial success in controlled experimental conditions. However, introducing objects lacking clear visual distinctions or testing under various environmental conditions would likely decrease accuracy.  
In contrast, Figure 13e-h display confusion matrices based on SWIR data, showing near-perfect accuracy. Unlike RGB results, the SWIR data produced minimal errors, with fake objects rarely misclassified as real, except in the case of bananas. The discrepancies with bananas occurred due to a different model type in the test data compared to the training data, which likely contributed to this error. Further training with a wider range of real and fake objects is expected to improve accuracy.  
Tables 1 and 2 present the per object detection results for RGB and SWIR, respectively. In the case of SWIR, the results are close to one for most objects except for the banana, clearly demonstrating that SWIR is significantly better at distinguishing materials, compared to RGB.  
Table 3 compares the accuracy of RGB and SWIR on the exact same objects. As seen, SWIR achieves nearly perfect accuracy, correctly identifying almost all cases without errors. In contrast, the RGB accuracy is relatively lower. Because the model trained on RGB data, achieving an accuracy of around 0.77%, it struggled to distinguish objects like bananas, where there is little visible difference between real and fake, resulting in poor differentiation.  
Figure 13: Confusion matrices of the RGB and SWIR image per-object classification.