## 4 Experiments
### Authentication Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Manipulation**} & \multicolumn{4}{c}{**Wavelet**} & \multicolumn{4}{c}{**SVD**} & \multicolumn{4}{c}{**RFVD**} & \multicolumn{4}{c}{**QFT**} \\ \cline{2-13}  & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** \\ \hline \multicolumn{13}{l}{_Original image-based reference hashing_} \\ \hline Gaussian noise & 0.6257 & 0.9500 & 0.7545 & 0.8442 & 0.8537 & 0.4773 & 0.6122 & 0.8501 & 0.8326 & 0.8211 & 0.8268 & 0.8991 & 0.8978 & 0.7951 & 0.8227 & 0.9241 \\ SatisfRepper & 0.5485 & 0.9773 & 0.7026 & 0.8043 & 0.8537 & 0.4773 & 0.6122 & 0.8507 & 0.8806 & 0.8119 & 0.7449 & 0.9088 & 0.8851 & 0.7727 & 0.8525 & 0.9184 \\ Gaussian blurring & 1.0000 & 0.8727 & 0.9320 & 0.9866 & 1.0000 & 0.4409 & 0.6120 & 0.9874 & 1.0000 & 0.7465 & 0.8549 & 0.9557 & 1.0000 & 0.7227 & 0.8591 & 0.9948 \\ Circular blurring & 1.0000 & 0.8733 & 0.9346 & 0.9787 & 1.0000 & 0.4364 & 0.6076 & 0.9852 & 0.9821 & 0.7604 & 0.8571 & 0.9447 & 1.0000 & 0.7272 & 0.8931 & 0.9948 \\ Motion blurring & 1.0000 & 0.8727 & 0.9346 & 0.9787 & 1.0000 & 0.4273 & 0.9876 & 0.8968 & 1.0000 & 0.7477 & 0.8585 & 0.9572 & 1.0000 & 0.7227 & 0.8991 & 0.9948 \\ Average filtering & 1.0000 & 0.8864 & 0.9398 & 0.9665 & 1.0000 & 0.4318 & 0.6023 & 0.9790 & 0.9598 & 0.7661 & 0.8520 & 0.9551 & 1.0000 & 0.7227 & 0.8591 & 0.9948 \\ Median filtering & 0.9667 & 0.9500 & 0.8038 & 0.9012 & 0.9988 & 0.4409 & 0.6010 & 0.9544 & 0.9399 & 0.7800 & 0.8579 & 0.9212 & 1.0000 & 0.7499 & 0.8512 & 0.9721 \\ Wiener filtering & 0.9847 & 0.8773 & 0.9279 & 0.9713 & 1.0000 & 0.4318 & 0.6023 & 0.9822 & 0.9880 & 0.7569 & 0.8571 & 0.9427 & 1.0000 & 0.7227 & 0.8991 & 0.9950 \\ Image sharpening & 0.7178 & 0.9364 & 0.8126 & 0.8827 & 0.9709 & 0.4545 & 0.6192 & 0.9368 & 0.9890 & 0.8073 & 0.8502 & 0.9153 & 0.8851 & 0.8583 & 0.8537 & 0.8537 & 0.8537 \\ Image scaling & 1.0000 & 0.8773 & 0.9346 & 0.9892 & 1.0000 & 0.4318 & 0.6022 & 0.9874 & 1.0000 & 0.7325 & 0.8496 & 0.9677 & 0.8858 & 0.9677 & 0.8852 & 0.9184 \\ Illumination correction & 0.5000 & 1.0000 & 0.6667 & 0.5593 & 0.5479 & 0.8318 & 0.6606 & 0.6754 & 0.9021 & 0.8028 & 0.8495 & 0.9073 & 0.6429 & 0.9000 & 0.7500 & 0.8498 \\ JPEG compression & 1.0000 & 0.4909 & 0.6585 & 0.9271 & 1.0000 & 0.4318 & 0.6032 & 0.9846 & 1.0000 & 0.3073 & 0.4702 & 0.9408 & 0.9273 & 0.6955 & 0.7948 & 0.9015 \\ \hline \multicolumn{13}{l}{_Multi-attacker reference hashing_} \\ \hline Gaussian noise & 0.8345 & 0.5273 & 0.6462 & 0.8465 & 0.8462 & 0.3000 & 0.4430 & 0.8846 & 0.9600 & 0.3303 & 0.4915 & 0.8948 & 0.9279 & 0.8773 & 0.9019 & 0.9588 \\ SatisfRepper & 0.7619 & 0.5818 & 0.6598 & 0.8046 & 0.9507 & 0.6136 & 0.7459 & 0.9263 & 0.97506 & 0.3208 & 0.4615 & 0.9557 & 0.9560 & 0.6090 & 0.8000 & 0.9355 \\ Gaussian blurring & 1.0000 & 0.6000 & 0.7500 & 0.9955 & 1.0000 & 0.6045 & 0.7535 & 0.9904 & 0.9927 & 0.6415 & 0.7794 & 0.9580 & 1.0000 & 0.6818 & 0.9952 \\ Circular blurring & 1.0000 & 0.4955 & 0.6266 & 0.9811 & 1.0000 & 0.6045 & 0.7535 & 0.9904 & 0.9926 & 0.6368 & 0.7759 & 0.9870 & 1.0000 & 0.6818 & 0.8108 & 0.9959 \\ Motion blurring & 1.0000 & 0.4909 & 0.6585 & 0.9481 & 1.0000 & 0.6000 & 0.7500 & 0.9955 & 0.8645 & 0.6147 & 0.7771 & 0.9857 & 1.0000 & 0.6818 & 0.8108 & 0.9952 \\ Average filtering & 1.0000 & 0.4955 & 0.6626 & 0.9709 & 1.0000 & 0.6091 & 0.7571 & 0.9955 & 0.9714 & 0.9149 & 0.4722 & 0.9270 & 1.0000 & 0.6818 & 0.8108 & 0.9959 \\ Median filtering & 0.9590 & 0.5313 & 0.6842 & 0.9013 & 0.9926 & 0.6091 & 0.7549 & 0.9803 & 1.0000 & 0.3211 & 0.4861 & 0.9258 & 1.0000 & 0.6818 & 0.8108 & 0.9990 \\ Wiener filtering & 1.0000 & 0.4969 & 0.6855 & 0.9703 & 1.0000 & 0.6045 & 0.7535 & 0.9901 &
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Manipulation**} & \multicolumn{4}{c}{**Wavelet**} & \multicolumn{4}{c}{**SVD**} & \multicolumn{4}{c}{**RFIVD**} & \multicolumn{4}{c}{**QFT**} \\ \cline{2-13}  & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** \\ \hline \multicolumn{13}{l}{_Original image-based reference hashing_} \\ \hline Gaussian noise & 0.7451 & 0.6623 & 0.8010 & 0.7909 & 0.8385 & 0.7015 & 0.7639 & 0.8825 & 0.9782 & 0.6830 & 0.8044 & 0.9201 & 0.8802 & 0.8965 & 0.8883 & 0.9520 \\ SatisfRepper & 0.8128 & 0.6481 & 0.7212 & 0.8307 & 0.8978 & 0.6963 & 0.7855 & 0.9164 & 0.9699 & 0.6329 & 0.7660 & 0.9282 & 0.8837 & 0.8856 & 0.8847 & 0.9572 \\ Gaussian blurring & 0.9694 & 0.5861 & 0.7305 & 0.9434 & 0.9397 & 0.6852 & 0.8811 & 0.9512 & 0.9502 & 0.6452 & 0.7685 & 0.9881 & 1.0000 & 0.8638 & 0.9269 & 0.9999 \\ Circular blurring & 0.9999 & 0.5999 & 0.7293 & 0.9526 & 0.9966 & 0.6939 & 0.8809 & 0.8274 & 0.8124 & 0.6856 & 0.7436 & 0.8467 & 1.0000 & 0.8638 & 0.9296 & 0.9999 \\ Motion blurring & 0.9745 & 0.8157 & 0.7285 & 0.9526 & 0.9952 & 0.6977 & 0.8078 & 0.9642 & 0.8972 & 0.6201 & 0.7604 & 0.9611 & 0.8603 & 0.9269 & 0.9999 \\ Average filtering & 0.8786 & 0.6231 & 0.7291 & 0.8917 & 0.8725 & 0.7179 & 0.7883 & 0.8853 & 0.6526 & 0.7738 & 0.7101 & 0.7739 & 1.0000 & 0.8638 & 0.9296 & 0.9999 \\ Median filtering & 0.8883 & 0.6503 & 0.7307 & 0.8457 & 0.9269 & 0.7048 & 0.8007 & 0.9047 & 0.7296 & 0.7216 & 0.7256 & 0.8880 & 1.0000 & 0.8649 & 0.9276 & 0.9939 \\ Wiener filtering & 0.8997 & 0.6155 & 0.7309 & 0.9055 & 0.9485 & 0.7015 & 0.8065 & 0.9212 & 0.8227 & 0.6921 & 0.7539 & 0.8506 & 1.0000 & 0.8638 & 0.9269 & 0.9980 \\ Image sharpening & 0.7194 & 0.7197 & 0.7168 & 0.7889 & 0.8099 & 0.7020 & 0.7028 & 0.7891 & 0.8665 & 0.8268 & 0.7295 & 0.8041 & 0.6656 & 0.9590 & 0.7227 & 0.8635 \\ Image scaling & 0.9686 & 0.5719 & 0.7241 & 0.9640 & 0.9592 & 0.6808 & 0.8056 & 0.9672 & 0.9581 & 0.6234 & 0.7553 & 0.9180 & 1.0000 & 0.8627 & 0.9963 & 0.9966 \\ Illumination correction & 0.5008 & 0.9978 & 0.6669 & 0.6063 & 0.6256 & 0.8573 & 0.7233 & 0.7541 & 0.9941 & 0.5579 & 0.7147 & 0.9810 & 0.8854 & 0.9085 & 0.9086 & 0.9616 \\ JPEG compression & 1.0000 & 0.4909 & 0.6585 & 0.9271 & 0.9676 & 0.6830 & 0.8008 & 0.9580 & 0.9565 & 0.6495 & 0.7376 & 0.9076 & 0.7148 & 0.9281 & 0.8706 & 0.8861 \\ \hline \multicolumn{13}{l}{_Multi-attractor reference hashing_} \\ \hline Gaussian noise & 0.7604 & 0.6599 & 0.7049 & 0.7993 & 0.8647 & 0.6961 & 0.7713 & 0.8902 & 0.9429 & 0.8638 & 0.9016 & 0.9578 & 0.9130 & 0.8922 & 0.9025 & 0.9646 \\ SatisfRepper & 0.8415 & 0.6326 & 0.7246 & 0.8407 & 0.9261 & 0.6961 & 0.9748 & 0.9202 & 0.9783 & 0.8497 & 0.9075 & 0.9639 & 0.9068 & 0.9854 & 0.9300 & 0.9641 \\ Gaussian blurring & 1.0000 & 0.5664 & 0.7232 & 0.9977 & 1.0000 & 0.6634 & 0.796 & 0.9807 & 0.9584 & 0.8046 & 0.8748 & 0.9481 & 1.0000 & 0.8758 & 0.9328 & 0.9999 \\ Circular blurring & 0.9943 & 0.5708 & 0.7253 & 0.9624 & 0.9951 & 0.6645 & 0.7969 & 0.9644 & 0.8596 & 0.8155 & 0.8370 & 0.9081 & 1.0000 & 0.8758 & 0.9328 & 0.9989 \\ Motion blurring & 1.0000 & 0.5641 & 0.7233 & 0.9800 & 1.0000 & 0.6656 & 0.7992 & 0.9857 & 0.9867 & 0.8070 & 0.8884 & 0.9618 & 1.0000 & 0.8758 & 0.9338 & 0.9989 \\ Average filtering & 0.9451 & 0.6002 & 0.7342 & 0.9201 & 0.9547 & 0.6852 & 0.7923 & 0 For performance analysis, we took wavelet-based and SVD-based image hashing to extract features and used the semi-supervised method to train \(\mathbf{W}\) for each content-preserving manipulations.