25-08-2017, 09:32 PM
We begin by considering an Artificial neural n/w architecture in which every node is connected to every node and these connect as are either excitatory or inhibitory or irrelevant.
A single node is insufficient for many patricidal problems and large number of nodes is frequently used. The way nodes are connected determines how computations proceed and constitutes an important early design decision by a neural network developer. A brief discussion of biological neural networks is relevant, prior to examining artificial neural network architectures. Different parts of the central nervous system are structured differently hence incorrect to claim that a single architecture models all neural processing. The cerebral cortex, where most processing is believed to occur, consists of five to seven layers of neurons with each layer supplying inputs to the next. However, layer boundaries are not strict and connections that cross layers are known to exit. Feedback pathways are also known to exists, e.g. between (to and fro) the visual context and the lateral genetical nucleus. Each neuron is connected with many, but not all, of the neighboring neurons within the some veto neurons that have overwhelming power of neutralizing the effects of a large number of excitatory inputs to a neuron. Some amount of indirect self-excitation also occurs - one node s activation excites its neighbor, which excites the first again. In the following sub sections, we discuss artificial neural network architectures, some of which derive inspiration from biological neural networks. Data secrity An Information-Theoretic Model for Steganography Steganography s goal is to conceal the presence of a secret message within an innocuous-looking communication.In other words, steganography consists of hiding a secret hiddentext message within a public covertext to obtain a stegotext in such a way that any observer (except, of cthese, the intended recipient) is unable to distinguish between a covertext with a hiddentext and one without. The model is perhaps best illustrated by Simmons Prisoners Problem . Alice and Bob are in jail,locked up in separate cells far apart from each other, and wish to devise an escape plan. Theyare allowed to communicate by means of sending authenticated messages via trusted ctheiers,provided they do not deal with escape plans. The ctheiers are agents of the warden Eve (the adversary) and will leak all communication to her. If Eve detects any sign of conspiracy, she will ution, except that it is generated from independently repeated experiments. This paper views steganography as information hiding with a passive adversary thwart the escape plans by transferring both prisoners to high-security cells from which nobody has ever escaped. Alice and Bob are well aware of these facts, so that before getting locked up, they have shared a few secret codewords that they are now going to exploit for adding a hidden meaning to their seemingly innocent messages. Alice and Bob succeed if they can exchange information allowing them to coordinate their escape and Eve does not become suspicious.Of these, Eve knows what a legitimate conversation among prisoners is like, and shealso knows about the tricks that prisoners apply to embed a hidden meaning in a seeminglyinnocent message. Following the approach of information theory, we capture this knowledgeby a probabilistic model, and view Eve s task of detecting hidden messages as a problem ofhypothesis testing.
A single node is insufficient for many patricidal problems and large number of nodes is frequently used. The way nodes are connected determines how computations proceed and constitutes an important early design decision by a neural network developer. A brief discussion of biological neural networks is relevant, prior to examining artificial neural network architectures. Different parts of the central nervous system are structured differently hence incorrect to claim that a single architecture models all neural processing. The cerebral cortex, where most processing is believed to occur, consists of five to seven layers of neurons with each layer supplying inputs to the next. However, layer boundaries are not strict and connections that cross layers are known to exit. Feedback pathways are also known to exists, e.g. between (to and fro) the visual context and the lateral genetical nucleus. Each neuron is connected with many, but not all, of the neighboring neurons within the some veto neurons that have overwhelming power of neutralizing the effects of a large number of excitatory inputs to a neuron. Some amount of indirect self-excitation also occurs - one node s activation excites its neighbor, which excites the first again. In the following sub sections, we discuss artificial neural network architectures, some of which derive inspiration from biological neural networks. Data secrity An Information-Theoretic Model for Steganography Steganography s goal is to conceal the presence of a secret message within an innocuous-looking communication.In other words, steganography consists of hiding a secret hiddentext message within a public covertext to obtain a stegotext in such a way that any observer (except, of cthese, the intended recipient) is unable to distinguish between a covertext with a hiddentext and one without. The model is perhaps best illustrated by Simmons Prisoners Problem . Alice and Bob are in jail,locked up in separate cells far apart from each other, and wish to devise an escape plan. Theyare allowed to communicate by means of sending authenticated messages via trusted ctheiers,provided they do not deal with escape plans. The ctheiers are agents of the warden Eve (the adversary) and will leak all communication to her. If Eve detects any sign of conspiracy, she will ution, except that it is generated from independently repeated experiments. This paper views steganography as information hiding with a passive adversary thwart the escape plans by transferring both prisoners to high-security cells from which nobody has ever escaped. Alice and Bob are well aware of these facts, so that before getting locked up, they have shared a few secret codewords that they are now going to exploit for adding a hidden meaning to their seemingly innocent messages. Alice and Bob succeed if they can exchange information allowing them to coordinate their escape and Eve does not become suspicious.Of these, Eve knows what a legitimate conversation among prisoners is like, and shealso knows about the tricks that prisoners apply to embed a hidden meaning in a seeminglyinnocent message. Following the approach of information theory, we capture this knowledgeby a probabilistic model, and view Eve s task of detecting hidden messages as a problem ofhypothesis testing.