# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 5 Experimental Results
### Overall Framework Test for Photo Copyright Identification  
To demonstrate the effectiveness of each module in the proposed framework, as shown in Table 6, _Image RoI Detection_ and _Image Verification_ are progressively applied to _Image hashing_. There are three identification results, _identified_, _unidentified_, and _misidentified_. _unidentified_ is only decided by _Image Verification_.  
(1) Image hashing only shows the identification results without _Image RoI Detection_. Due to randomness, most image collages with one image are correctly _identified_, whereas image collages with more than two images are prone to be _misidentified_. In (2) Image RoI detection + Image hashing, images are separated well from images collages and this method achieves _identified_ of more than 80%, but there still remains a _misidentified_ rate of 11% at least. The last one, (3) Image RoI detection + Image hashing + Image verification, can distinguish _unidentified_ from _misidentified_. By applying _Image Verification_, the proposed framework reduces the number of _misidentified_ collages significantly and the number of _misidentified_ collages which can be categorized as the false positive rate is reduced to 2.8% as a consequence.  
Figure 12 shows identification results of HoG, BinaryGAN, and the proposed framework. The number of results is the same as the number of images in a collage, meaning that the image detector works well to separate images from an image collage. The performance of the proposed framework is especially remarkable in comparison with the other methods in the second results of Figure 12. While the other methods give incorrect images similar to original images in terms of shape or color, the proposed framework correctly matches images by distinguishing image details. Lastly, only one image is determined as _misidentified_ by reducing false positives.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Method & \multicolumn{3}{c}{(1) Image hashing only} \\ \hline Result & _Identified_ & _Unidentified_ & _Misidentified_ \\ \hline HoG & 3.4\% & - & 96.6\% \\ BinaryGAN & 7.4\% & - & 92.6\% \\ Proposed & 8.2\% & - & 91.8\% \\ \hline \hline \end{tabular} \begin{tabular}{c c c} \hline \hline Method & \multicolumn{3}{c}{(2) Image RoI detection} \\  & & + Image hashing \\ \hline Result & _Identified_ & _Unidentified_ & _Misidentified_ \\ \hline HoG & 79.4\% & - & 20.6\% \\ BinaryGAN & 83.1\% & - & 16.9\% \\ Proposed & 88.7\% & - & 11.3\% \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c} \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Method}} & \multicolumn{3}{c}{(3) Image RoI detection} \\  & & + Image hashing & \\  & & + Image verification & \\ \hline Result & _Identified_ & _Unidentified_ & _Misidentified_ \\ \hline HoG & 79.4\% & 16.4\% & 4.2\% \\ BinaryGAN & 83.1\% & 13.6\% & 3.3\% \\ Proposed & 88.7\% & 8.5\% & 2.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation tests of photo copyright identification framework by using HoG [11], BinaryGAN [10], and the proposed image hashing with the binary descriptor size 512. The identification results are divided into _identified_, _unidentified_, and _misidentified_. _Identified_ means the correct identification, and _unidentified_ defers the image identification. _Misidentified_ is the identification error (false positive rate), which should be reduced.  
Figure 12: Copyright identification results of image collage queries. (**a**–**c**) indicate the results of HoG [11], BinaryGAN [10], and the proposed hashing method, respectively. Green, Red, and blue boxes mean _identified_, _misidentified_, and _unidentified_ results, respectively.