Abstract With the rapid development of image editing technology, tampering with images has become easier. Maliciously tampered lead to serious security problems (e.g., when used as evidence). The current mainstream methods are divided into three types which copy‐move, splicing and removal. Many detection can only detect one type tampering. Additionally, some learn features by suppressing conten...

A eld computer is a (spatial) continuum-limit neural net (MacLennan 1987). We investigate eld computers whose temporal dynamics is also continuum-limit, being governed by an integro-diierential equation. We prove that even when they are purely linear, such systems are computationally universal. The \trick" used to get such universal (and therefore in general nonlinear) behavior is quite similar...

This work presents a sample of what evolved neural net circuit modules using the socalled "CoDi-1Bit" neural net model [5] can do. This work is part of an 8 year research project at ATR which aims to build an artificial brain containing a billion neurons by the year 2001, that will be used to control the behaviors of a kitten robot "Robokoneko" [2][3][4]. It looks as though the figure is more l...

Why Neural Nets? ..................................................................................................................1 What is a Neural Net?.............................................................................................................2 The Perceptron....................................................................................................................3 ...

Neural Logic Network or Neulonet is a hybrid of neural network expert systems. Its strength lies in its ability to learn and to represent human logic in decision making using component net rules. The technique originally employed in neulonet learning is backpropagation. However, the resulting weight adjustments will lead to a loss in the logic of the net rules. A new technique is now developed ...

This position paper discusses the evolution of multi-module neural net systems, where the number of neural net modules is up to ten million (i.e. an "artificial brain"). ATR's "CAM-Brain" Project [de Garis 1993, 1996] has progressed to the point where it is technically possible (using a new FPGA (Field Programmable Gate Array) based evolvable hardware (EHW or E-Hard) system to be completed by t...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

It is time to locate Connectionist representation theory in the new wave of robotics research. The utility of representations developed in Artiicial Neural Networks during learning has been demonstrated in Cognitive Science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot ...

This paper compares three mathematical models for surgical decisions on head injury patients. A logistic regression and two neural network models were developed using a large clinical database. Using randomly selected 9480 cases as the training group and another 3160 cases as the validation group. We evaluated the performance of a logistic regression model, a multi-layer perceptron (MLP) neural...