# AI Makes Crypto Evolve
## 7 AIEC
### Neural Cryptography  
Neural cryptography is a cutting-edge cryptographic paradigm, wherein key exchange depends on mutual learning between two different NNs fed by identical input patterns, which update their weights according to predefined rules [143]. It is the most significant trend in the realm of AIEC [144; 145; 146; 147]. It has converged with other emerging trends such as chaotic cryptography [148] and DNA cryptography [149]. Neural cryptography has been successfully applied to different content types such as text [149; 150] or binary messages [151; 152]. In this subsection, we study the current state of neural cryptography. Neural Encryption can also be used on multimedia applications such as Image Encryption with applications ranging from medicine through to encrypting satellite imaging [153]. This is seen in the Chaos Sequence generated by Cellular NNs and it's application in Encrypting Images. One such application of this uses block encryption on an image and then strengthens it with a 'radius basis chaotic neural network' [154]. A wavelet based Chaotic Neural Network can be leveraged for image encryption. Specifically, WCNN provides two key security features along with data compression. The first one is the encryption of the image itself and the second is the comparison properties between the scrambled images. WCNN  
Figure 7: AIRC: Adopts ML and DL for Encryption. -based encryption is strong because similar keys encrypted with different keys have little to no correlation with each other [155]. Furthermore, Cellular NN's chaotic properties can be combined with compressive sensing (which can achieve synchronous compression and encryption) in order to create an encryption algorithm. The measurement matrix for this algorithm is implemented through Lissajous map and is controlled by the index sequence of the CNN. This encryption scheme has a large key space, high key sensitivity and a strong compression system [156]. Image encryption can be accomplished by the Chaotic Dynamics present in the HNNs. The cryptosystem uses chaotic signals generated by the human body which are developed and implemented as FPGA hardware. During testing it was found that the correlation co-efficient for encrypted images with this method was close to zero. thereby proving that it is quite robust [157]. Rather than relying on key exchange, another approach to encrypt digital media using NNs is to use random substitutes and impurity additions onto the image. Once the image is received, ANNs will decrypt the image and extract the original image [158]. The Hermite chaotic NN has impressive application in the area of image encryption--specifically medical imaging [159]. First, the chaotic sequences are created using logistic mapping which is used to train a Hermite chaotic NN. The image is encrypted using two streams of keys generated by the NN. Experiments with this encryption system show a strong algorithm which is resistant to statistical analysis, and has a large key space and good sensitivity [160]. Another way NNs can be used for image encryption is through Memristive bidirectional associative memory neural networks. The model relies on leakage delays and time varying delays; it is used to develop a color image encryption algorithm. This algorithm has a large key space and resists attacks such as brute force and differential attacks [161]. It can also be applied by using an RBG plane where the first stage of this process involves Logistic mapping on all three planes of the image. Then, by using the chaotic functions in NNs present in the Newton-Leipbik chaotic system, the weights for the pixels can be changed and the output of the previous stage is used as the input of this step. This creates a strong encryption system which is resistant to attacks [162]. Image encryption can be accomplished by the Chaotic Dynamics present in HNNs. The cryptosystem uses chaotic signals generated by the human body which are developed and implemented as FPGA hardware.