# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Bio-Inspired AI  
Bio-inspired learning is implemented in a wide range of fields, with a common one being computer vision. [PERSON] et al. [273] proposed a bio-inspired system for Visual Attention Prediction (VAP) by leveraging both low-level contrast and high-level semantic features. Another proposal in the computer vision realm was made by [PERSON] et al. [274], where a combination of neural networks and collected eye-tracking data was utilized to determine if and why a face is perceived as beautiful. Extensive tests on this method using popular datasets demonstrated superior performance.  
Reinforcement learning can also see great improvements by pairing it with bio-inspired techniques. For example, [PERSON] et al. [275] proposed an autonomous navigation algorithm using visual sensors and auxiliary tasks to balance out the common problem of sparse rewards in standard reinforcement learning. Another proposal by [PERSON] et al. [276] used reinforcement learning to control a mobile hummingbird robot to perform biological-based maneuvers such as rapid escapes and tight body flips.  
Bio-inspired learning can even see use in the IoT space. [PERSON] et al. [277] proposed a system for optimizing IoT services to reduce service time and energy consumption inspired by the cooperative nature of the various systems in a biological organism. [PERSON] et al. [278] also performed work with spam email detection with standard classification models.  
Since AI techniques can often be very technical and confusing to newcomers, [PERSON] et al. [279] developed an interactive learning environment for bio-inspired AI called BOLE. This system works with unmanned aerial vehicle paths.  
Figure 4 demonstrates how current trends predict future trends. In the left column, icons are joined to form new icons for current trends. In the right column, different icons are joined in a similar vein to create predicted future trends. Arrows join the two columns to show how current trends predict future ones.  
The trends in Figure 4 form the following two main lines:  
1. The significant role of neural networks in the current state of chaotic image encryption. This role is highlighted by the role of neural networks in the three aspects of chaotic image encryption, namely _chaos_, _image_, and _encryption_. The role of neural networks in the creation and exploitation of chaos is studied in Section 5.1. Section 5.2 discusses the role of neural networks in image processing. Furthermore, the role of neural computing in encryption is highlighted in Section 5.3. The next few subsections investigate how neural networks appear in the intersections of these aspects.
2. The significant impact of quantum technology and biological inspirations on the future of neural networks. Sections 5.10 and 5.11 discuss the impact of quantum technology and biotechnology on the future of AI, and especially neural networks.  
The above two lines clearly will converge at bio-inspired/quantum-inspired neural chaotic image encryption.  
Figure 4: Chaotic Image Encryption Trends (Present and Future).