# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 4 Peace: Coexistence and Alliance
### Alliance
#### 4.2.1 The Role of NNs in Cryptography
Research has also been conducted to hide messages within sound [112]. To accomplish sound stenography, SMO's are used again with 27 clusters, 1 for every letter in the alphabet and then a cluster for the space between words [112]. * **Visual Cryptography:*
* One drawback of visual cryptography is its lack of evaluation criteria [113]. One group proposed a method of evaluating the desirable results of visual cryptography would be encryption-inconsistency and decryption-consistency [113]. Visual cryptography via NNs can be achieved by passing a Q'tron NN a set of greyscale images and the output be a set of binary images [114; 115]. Other types of NNs used for visual cryptography includes Pi-Sigma NNs, which is a double layered feed forward
network [116]. Allowing for fewer communications between sender and receiver with higher security [116]. * **Technological Applications:*
* The recent literature comes with several successful applications of neural cryptography in the technology. Some of these applications are studied below. * **Applications in Industry:*
* A large contribution to NN in technology comes from its applications for secure wireless communication [108; 117; 118]. After proof of its security was published [119]. Particularly, NN have been used with Fast Handover Protocol in place of MIPv6 to replace its short comings, allow the encryption of large scale satellite images for secure transmission and decryption efficiently and lightweight implementation for key systems in an IoT environment [117; 120; 121]. Other applications of Neural Cryptography has allowed for homomorphic encryption to be applied to cloud services for secure communication and noise compression, as well as intelligent transportation systems to allow confidentiality of personal information [122; 123]. Finally, chaotic NNs has seem many applications as well [124; 125; 126]. To note, hyper chaotic systems and chaotic Feistel transform and time synchronization with multiple dimensions have allowed the resistance of plain text attacks and brute force attacks within the physical layer [125; 126]. * **Applications in Medical Technologies:*
* Applications of neural cryptography in the field of medicine have come from the requirement of keeping patient images confidential [127]. One approach uses a Hermite Chaotic NN in two rounds, first a chaotic sequence is generated from a logical mapping and used to train the NN, then the image is passed into the network to generate a key for encryption [128]. Other methods of security proposed involve using the Region of Non-Interest in the image in order to watermark the image [127]. * A look at the literature shows that different kinds of NNs are useful for different applications. NN type selection is critical to the ability of neural cryptography to be successful, one group has even used NNs to effectively create new cryptography based off NN training [129]. Investigated the use of Complex-valued tree parity machines in order to perform key synchronizations and how CVTPM's can be seen as more secured to create key synchronizations than using simple tree parity machines [130]. Achieved postquantum key exchange protocols by using NNs in order to augment Diffie-Hellman key exchange protocols by using multivariate cryptosystems [131]. Explored the relationship between cryptographic functions and the learning abilities of RNN [132; 133]. Used Principle Component Analysis NNs to generate random numbers for a chaos encryption system [134]. Other groups have experimented with cellular NNs with iterative interchangeability to produce encryption that allows flat historgrams for randomness and bias [135]. Back-propogating NNs in order to provide strong image compression-encryption using a fractional-order hyperchaotic system [136]. unbounded inertia NNs with input saturation in order to obtain good cryptographic properties [137]. Memristive bidirectional associative memory NNs for colored image encryption [138].