# AI Makes Crypto Evolve
## 7 AIEC
### Neural Cryptography
During testing, it was found that the correlation coefficent for encrypted images with this method was close to zero, thereby proving that it is quite robust [157]. Fractional-Order Quantum Neural Networks (QNNs) provide another source for image encryption. This system uses a technique known as the anamorphic fractional Fourier Transformation which increases the leeway offered by the optical cryptosystem. The chaos order/sequence generated from the QCNN is used to provide a complex key space which can be used to encrypt the image and provides robust protection against attacks such as noise and occlusion [163]. Furthermore, combining Hyper Chaotic systems from cellular NNs along with encryption schemes that use Latin squares was shown to provide better security than traditional encryption methods for Grayscale image encryption [164]. Even though synchronization can be archived through the NN synchronization protocol, its performance can still be affected by delays. Asymptotical synchronization of the unbounded delayed inertial NNs model can be used to solve problem of unbounded delay within the synchronization system [165]. The encryption algorithm based on these NNs provides relatively more secure communication since it requires a hybrid input from the response system. Furthermore, NN can be leveraged for image encryption and compression. The middle layer in the NNs can be represented as the compressed image [166]. The compressing process occurs during the transformation from the input layer to the middle layer, and the decompressing process occurs from the middle layer to the output layer. Then, it can XOR with some chaotic functions to enhance security in encryption. Thereare many types of lag in the synchronization system. It is known that NNs can be applied to perform synchronization for communication. They can also be used to perform lag synchronization [167]. The reason for lag synchronization is because complete synchronization is hard to achieve due to transmission delay. As a result, lag synchronization may be preferred. Switched NNs can be used to solve globally exponential lag synchronization problems by adding an additional controller based on the neuron activation function. These NNs can perform encryptions in the same way as other NNs for synchronization systems. The traditional scrambling-diffusion structure for image encryption separates scrambling and diffusion into two processes and makes them available for hackers to decrypt simultaneously. Strong encryption must exist for both processes to resist decryption from attackers. Neural networks can be applied here with the chaotic function to provide robust and secure encryption for the diffusion process [168]. Another application of NNs in encryption comes with looking at ANNs and key distributions for symmetric encryption. The application of TPM which is a special type of ANN was used to establish a secret key exchange protocol. An interesting point which is to be noted is much of the TPM learning derived from changing the weights of TPM and not changing the inputs. This implies that the key exchange protocol is difficult to decipher [169]. The implementation of chaotic NNs extends to the public key cryptosystem, especially the Diffie-Hellman PKI. There exists a one way function between the chaotic sequence of NNs and the Overstored Hopfield Neural Network (OHNN). The Diffie-Helman public key cryptosystem can be implemented with changes in the synaptic matrix and changes of OHNN with the private key being the permutation operation of the synaptic matrix and the public key being the neural synaptic matrix after the permutation [170]. Another way NNs can be used as an encryption system is through the time delays between different neurons in the original data. This form of encryption has a wide array of applications ranging from using it as a one way function to generate a public key from a private one to using it as an encryption mechanism for communication between two entities [171].