## 7 Conclusions  
This paper proposes a novel fragile scheme named SR-HTR based on watermarking for color image authentication and self-recovery with high tampering rates. The image protection and extraction method can be implemented in three different variants (1-LSB, 2-LSB or 3-LSB), where it is possible to embed multiple copies (three, six or nine, respectively) of the recovery watermarks, and thus to increase the robustness of the scheme to the tampering coincidence problem. The evaluation of the three embedding-extraction variants of the novel method was carried out. The embedding watermark scheme uses a pseudo-random sequence to embed the recovery watermarks in different blocks to increase the watermarked image's objective quality. During the evaluation of the results, various alterations were investigated at different tampering rates (from 10% to 90%) with irregular and multiple alterations. Finally, during the recovery process, the 2-LSB embedding scheme was selected as a balanced solution between the quality of the watermarked image and the recovered images' quality for different alterations. The experimental results have shown good quality for the watermarked images obtained by the novel SR-HTR framework compared with state-of-the-art methods. Additionally, the novel scheme has demonstrated a good performance during the detection of regular, irregular and multiple alterations, resulting in _Precision_ and _Recall_ metrics higher than 0.9. The designed SR-HTR has shown excellent performance in reconstructing the alterations at different tampering rates (from 10% to 90%), which is superior to other state-of-the-art methods. Additionally, in cases of multiple and irregular alterations, the novel color image authentication and self-recovery framework has shown an excellent performance, maintaining high objective criteria values as well as great visual perception via the HVS. As future work, further investigations should be performed to resist different intentional attacks like cropping, scaling and rotation, and unintentional attacks such as JPEG compression. Additionally, for fast processing in real-time environments, we will consider the possibility of designing parallel fragile watermarking schemes implemented in _graphics processing units_ (GPU) or multicore _central processing units_ (CPU), based on adversarial examples, which have demonstrated remarkable performance in solving similar problems in image and audio domains [42], avoiding weaknesses presented by _deep neural networks_ (DNNs) where if an image is transformed slightly, will be incorrectly classified by a DNN even when the changes are small and unnoticed by the human eye [43]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **SR-HTR** & \begin{tabular}{c} **[PERSON]** \\ **[**36**]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **[**23**]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **[**2**]** \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON]** \\ **[**24**]** \\ \end{tabular} &
\begin{tabular}{c} **[PERSON]** \\ **[**10**]** \\ \end{tabular} \\ \hline \multirow{4}{*}{PSNR} & Kodak-4 & 25.90 & 19.42 & 10.01 & 10.25 & 9.97 & 11.22 \\  & Kodak-10 & 30.21 & 32.05 & 24.50 & 20.27 & 18.97 & 26.17 \\  & Kodak-17 & 26.76 & 20.95 & 11.33 & 11.46 & 11.20 & 13.07 \\  & Kodak-18 & 27.91 & 28.97 & 19.17 & 17.49 & 17.20 & 22.85 \\ \hline \multirow{4}{*}{SSIM} & Kodak-4 & 0.6883 & 0.3433 & 0.1961 & 0.1429 & 0.1341 & 0.1548 \\  & Kodak-10 & 0.9130 & 0.9197 & 0.9150 & 0.7680 & 0.7818 & 0.8658 \\  & Kodak-17 & 0.7661 & 0.4994 & 0.3858 & 0.2650 & 0.2602 & 0.2933 \\  & Kodak-18 & 0.8848 & 0.8868 & 0.8553 & 0.7187 & 0.7262 & 0.8174 \\ \hline \multirow{4}{*}{PSNR-HVS-M} & Kodak-4 & 21.69 & 14.78 & 11.21 & 8.21 & 11.27 & 9.23 \\  & Kodak-10 & 26.52 & 30.26 & 24.06 & 16.67 & 15.76 & 23.65 \\ \cline{1-1}  & Kodak-17 & 23.91 & 16.77 & 10.42 & 8.37 & 10.27 & 10.13 \\ \cline{1-1}  & Kodak-18 & 25.66 & 27.51 & 17.68 & 14.44 & 14.27 & 20.61 \\ \hline \hline \end{tabular}
\end{table}