# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 4 Experiment Results
When separating DNA extraction and database insertion times, the insertion time for bitmap-based and deep learning-based methods is negligible. In contrast, image processing-based methods incurred significant insertion delays, taking 1.5 to 2 times longer than DNA extraction. This is because image processing-based methods generate hundreds of high-dimensional feature vectors per DNA. To mitigate this limitation, enhancing parallelism for database insertion in image processing-based methods is essential. By contrast, the extraction process is embarrassingly parallel for filtering target images, giving all techniques the potential for performance improvements. For the Autoencoder method, training time was excluded from the measurement. Including training would add several hours to the overall build time. Even when comparing DNA extraction times alone, the bitmap-based method demonstrated the best performance. Image processing methods and some lightweight deep learning methods (e.g., Autoencoder, MobileNet) also achieved reasonable overheads of less than 100 s. However, the VGG method required nearly 300 s, due to its deep network architecture with numerous layers, which increases computational demand for DNA extraction. Figure 6: Comparison of DNA DB build time. In summary, for environments where database insertion time is critical, bitmap-based and deep learning-based methods impose minimal overhead and are thus more suitable. Conversely, image processing-based methods exhibit considerable overhead during insertion. For scenarios prioritizing DNA extraction speed, bitmap-based and image processing-based methods outperform deep learning techniques. Notably, the bitmap-based Nabla method required only 3.5 ms per image for DNA extraction, even when additional DNA was generated for vector indexing. Similarly, the image processing-based ORB method achieved an extraction time of approximately 5.9 ms per image. However, when accounting for the large number of feature vectors generated per DNA, the total time, including database insertion, increased to approximately 20 ms. The DNA search time consists of both DNA extraction time and database query time. Figure 7 illustrates the total search time for 161,062 images from the COCO dataset, excluding the 3000 filtering target images used to construct the DNA database. As shown in the figure, the search time trends are consistent with those observed during DNA database construction. Except for image processing-based methods, the database query time is negligible compared to the DNA extraction time. The bitmap-based Nabla method achieved a search time of approximately 3.5 ms per image, making it 3 to 27 times faster than other techniques. This superior performance stems from the crucial role of DNA extraction time in search efficiency. Since Nabla significantly reduces DNA extraction time, it directly accelerates the overall search process. For image processing-based methods such as SIFT and ORB, the search time accounted for 60% and 83%, respectively, of the DNA extraction time. This is because these methods generate hundreds of high-dimensional feature vectors per DNA, increasing the computational cost of database queries. Deep learning-based methods exhibited higher DNA extraction times, with VGG being the slowest due to its deep network architecture. Notably, the size of the DNA itself, regardless of whether it is derived from deep learning- or bitmap-based methods, had minimal impact on the extraction speed. To quantitatively compare the detection accuracy of each technique, the detection rates were evaluated using 20 transformed images from the COCO dataset. Figure 8 plots the true detection rate (TDR) and false detection rate (FDR) for the bitmap-based Nabla method as a function of the cosine similarity threshold between DNA vectors. The results show that increasing the threshold reduces both the TDR and FDR. Figure 7: Comparison of DNA DB search time.