# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 4 Discussion  
In this section, the advantages, disadvantages, and applications of each of the aforementioned image encryption techniques are explored. Each image encryption technique provides a level of security and privacy, yet each of the techniques has some pros and cons. Table 6 summarizes the strengths and weaknesses of different image encryption algorithms. In the following, we elaborate the strengths and weaknesses of different image encryption algorithms. Traditional encryption algorithms are still the most commonly recommended and used algorithms to encrypt images over networks. Their security was assessed and proven through standardized testing and cryptanalysis. Their downside is that they typically require extensive computation time and significant processing power. Furthermore, traditional encryption algorithms fail when attempting to decrypt cipher images that have some corrupted pixels since the decrypted data must be the exact plaintext data. Traditionalencryption produces faulty decrypted images when decrypting a cipher image that has been affected by noise or has lost pixels, whether due to attacks or transmission errors. In multimedia applications, the content of an image is important, and minor distortions in the original image are acceptable [66]. Chaotic systems-based image encryption algorithms provide excellent security due to their high sensitivity to the initial parameters, fast computation speed, and minimal complexity. A potential disadvantage of chaotic systems-based image encryption algorithms is the limited range of chaotic behavior for some chaotic maps such as logistic and tent maps. Moreover, certain chaotic maps, such as the Baker map, have been known to be vulnerable to chosen-plaintext attacks. This vulnerability could be mitigated by adding other layers of security, such as utilizing an IV or nonce or incorporating multiple chaotic maps. Furthermore, some chaotic maps are known to exhibit periodic behavior, meaning that after a certain number of permutation iterations, the image's pixels will return to their original position. DNA-based image encryption is an innovative concept with high-security levels; however, it is still largely experimental and primarily explored in academic research. It has not yet found widespread practical application in data encryption scenarios. Furthermore, it requires specialized laboratory equipment to handle DNA synthesis and sequencing. This level of complexity hinders the practical application of DNA-based image encryption. Neural network-based image encryption algorithms have primarily emerged due to advancements in artificial neural networks and deep learning. Once effectively trained, they can provide a quick means of image encryption. However, the level of security they offer remains a relatively unexplored domain. Further research and analysis on the security of neural network-based image encryption algorithms are imperative to comprehensively understand and quantify the robustness of these algorithms against potential threats and attacks. Frequency domain-based image encryption techniques utilize confusion and diffusion operations within the frequency domain of an image. These techniques are computationally more expensive compared to chaotic system-based image encryption algorithms, and conceptually seem to provide a high-security level. Nevertheless, the security analysis of frequency domain-based image encryption methods remains unexplored in the literature. Compressive sensing-based image encryption algorithms can reduce the size of image data and add an improved level of security to image transmission. These algorithms have the disadvantage of high computational overhead resulting from combining compressive sensing and encryption operations. Furthermore, the effectiveness of compressive sensing depends on choosing the measurement matrix, which can affect the overall performance and efficiency of the algorithm.