## 3 Approach
### Material Analysis and Spectral Band Selection  
In this study, we proposed an approach to address the challenge of differentiating objects with similar appearances by using SWIR wavelengths to analyze material-specific characteristics. RGB cameras, which rely on visible spectrum wavelengths, often struggle with this task. However, by leveraging SWIR wavelengths absorbed by moisture, we enabled robust differentiation between materials, allowing us to distinguish a mannequin from a human, even when their appearances are similar. As shown in Figure 7a, a mannequin and a human have highly similar appearances. Using conventional object detection models, such as YOLO [32] and DETR [33], they are both detected as humans. Although fine-tuning could enable the models to distinguish them based on subtle differences in appearance, this would only allow the model to determine that specific situation shown in the training dataset and would not generalize well. In addition, if the distance from the object increases or if the objects are designed to look very similar, it becomes impossible to distinguish them using only information from the visible spectrum. Therefore, additional information is needed to differentiate objects with similar appearances. To do that, we used data acquired from the SWIR camera; the reflection intensity values for each wavelength were obtained, and the differences were analyzed according to the characteristics of the objects. By utilizing the phenomenon where reflectivity varies with the presence of moisture, intensity values are gathered for areas with moisture to analyze the relative differences between wavelengths. Figure 6: Examples of SWIR-range data: (**a**) SWIR-range data captured using a standard SWIR camera and (**b**) SWIR-range data acquired using a snapshot SWIR camera. Figure 7: Zero-shot object detection results on Human vs Others Dataset (**a**) with YOLO [32] (**b**) and DETR [33] (**c**). Both models detect each object as a “person” with high confidence scores, indicating that mannequins share significant visual similarities with humans. **Spectral Band Selection:** Inspired by the paper of [PERSON] et al. [1] on classifying waste materials using SWIR reflectance measurements, we used the properties of water to distinguish between real and fake objects. We looked for wavelengths that had a direct effect on water and humidity. The 1095 nm wavelength is part of the NIR spectrum and shows weak interaction with water, allowing light to pass through moisture with minimal absorption. In contrast, the 1550 nm and 1580 nm wavelengths exhibit stronger absorption characteristics due to water, creating a clear distinction between human and mannequin skin. The 1345 nm wavelength can be considered to have an absorption characteristic that is intermediate between 1095 nm and 1580 nm, showing some water absorption properties in moisture analysis without the strong absorption characteristics of 1580 nm. To validate these findings, each pixel was annotated in a polygonal form, covering areas such as the head, clothing, arms, and legs. For the head areas of both the mannequin and the person, pixel values were extracted, and the average intensity for the region was calculated across all frames of the data and presented as the average intensity per frame (Figure 8). Although there was a difference in the absolute intensity value depending on the lighting, the trend of the relative intensity of the wavelength acquired for interpretation was confirmed to be similar. Our analysis revealed that the key difference lies in their interaction with moisture: human skin contains water, while mannequin skin does not. At 1095 nm (shown by the blue line), light passes through with minimal absorption, while, at 1580 nm (shown by the green line), light is strongly absorbed by moisture. This creates a distinct pattern: mannequin skin shows high intensity at 1580 nm due to the absence of moisture, while human skin shows lower intensity at this wavelength due to water absorption.