# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 4 Experiment Results  
In this section, we conduct a quantitative analysis of three different types of image DNA techniques using various performance metrics through experiments. For the bitmap-based methods, two configurations of the Nabla DNA technique were used: low-resolution Nabla (x8d4x8) and high-resolution Nabla (x8d8x16). The image processing-based methods included SIFT and ORB, while the deep learning-based methods consisted of Autoencoder, MobileNet, and VGG. Table 3 summarizes the image DNA techniques employed in this study. The code and datasets used for the experiments are publicly available on GitHub [13], versioned at commit 0 ca06 ef. Figure 5 illustrates a heatmap of DNA similarities for the Kaggle dataset, which comprises 34 equivalent image pairs and 19 non-equivalent image pairs. Cosine similarity was used to measure DNA similarity. For equivalent image pairs, similarities below 0.9 (to \(-\)1) are represented with a gradient of red, while for non-equivalent pairs, similarities from 0.5 to 1 are shown in blue. Darker red indicates low DNA similarity for equivalent images, signifying poor true detection rates, while darker blue indicates high DNA similarity for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **DNA** & **Experiment Settings** \\ \hline Bitmap-based & Nabla & \begin{tabular}{c} Configured with various resolutions and depths ranging from \\ x4d4x8 to x8d8x16 \\ \end{tabular}  \\ \hline \begin{tabular}{c} Image Processing- \\ based \\ \end{tabular}  & \begin{tabular}{c} SIFT \\ ORB \\ \end{tabular}  & Using SIFT class within the Python3 OpenCV package \\ \hline \multirow{6}{*}{ \begin{tabular}{c} Deep Learning- \\ based \\ \end{tabular}  } & \multirow{3}{*}{\ \begin{tabular}{c} MobileNet \\ \end{tabular}  } & - &
\begin{tabular}{c} The encoder consists of two Conv2D layers in Keras, and the \\ decoder consists of two Conv2 DTranspose layers. \\ -non-equivalent pairs, signifying elevated false detection rates. The closer the colors are to white, the better the performance. Figure 5: DNA similarity heatmap for equivalent and non-equivalent image pairs. Cell colors transition from red (\(-1.0\)) to white (\(0.9\)) for equivalent pairs and from blue (\(1.0\)) to white (\(0.5\)) for non-equivalent pairs, representing increasing and decreasing DNA similarity, respectively. Cells exceeding \(0.9\) in red or below \(0.5\) in blue do not display gradients. As shown in Figure 5, ORB and Autoencoder (u16) achieved relatively good overall results. However, none of the methods could perfectly detect image equivalence. Many equivalent image pairs exhibited low cosine similarity across all techniques, likely due to the nature of the dataset. The Kaggle equivalent image pairs often included the same object photographed from different angles or heavily cropped images, resulting in low similarity for bitmap-based and image processing-based methods. Deep learning-based DNA techniques, which excel at distinguishing image classification and similarity, generally demonstrate higher cosine similarity. In particular, the Autoencoder trained on a limited dataset achieves a notably high true detection rate. However, apart from SIFT, all other methods show relatively high DNA similarity for non-equivalent image pairs, indicating a higher likelihood of incorrectly judging image equivalence. Figure 6 illustrates the time required to construct DNA databases for each technique, using 3000 randomly selected images from the COCO dataset as the filtering target. The database construction time is composed of DNA extraction time and the time to insert the DNA into the VDB. DNA extraction time in our experiments excludes I/O delays, as image files are read from the buffer cache instead of secondary storage [32]. As shown in the figure, the bitmap-based Nabla method outperformed other techniques, achieving speeds that were 5.6 to 30.5 times faster. This superior performance is attributed to its reduced DNA extraction time and smaller DNA size, which collectively contribute to faster processing.