In this paper, we solve the edge enhancement problem using an intelligent approach. We use a multilayer neural network
based on multi-valued neurons (MLMVN) as an intelligent edge enhancer. The problem of neural edge enhancement
using a classical multilayer feedforward neural network (MLF) was already considered by some authors. Since MLMVN
significantly outperforms MLF in terms of learning speed, number of parameters employed, and generalization
capability, it is very attractive to apply it for solving the edge enhancement problem.
The main result which is presented in the paper, is the proven ability of MLMVN to enhance edges corresponding
to a certain edge detection operator. Moreover, it is possible to enhance edges on noisy images ignoring a noisy texture.
It is shown that to learn any edge detection operator using MLMVN, only a single image is required for learning
purposes.
The most important conclusion is that a neural network can learn different edge detection operators from a single
example and then process those images that did not participate in the learning process detecting edges specifically
corresponding to the learned operator with a high accuracy.
Popular press and congressional record report a belief by the intelligence community that Al Qaeda members communicate through messages embedded invisibly in images shared via the Internet. This is certainly plausible as steganography has a rich history of military and civilian use. Current signature-based approaches for detecting the presence of hidden messages rely on discerning "footprints" of steganographic tools. Of greater recent concern is detecting the use of novel tools for which no signature has been established. This research addresses this concern by using a method for detecting anomalies in seemingly innocuous images, applying a genetic algorithm within a computational immune system to leverage powerful image processing through wavelet analysis. The sensors developed with this system demonstrated a surprising level of capability to detect the use of steganographic tools for which the system had no previous exposure, including one tool designed to be statistically stealthy.
Current signature-based approaches for detecting the presence of hidden messages in digital files - the initial step in steganalysis - rely on discerning "footprints" of steganographic tools. Of greater recent concern is detecting the use of novel proprietary or "home-grown" tools for which no signature has been established. This research focuses on detecting anomalies in seemingly innocuous images, applying a genetic algorithm within a computational immune system to leverage powerful image processing through wavelet analysis. The sensors developed with this new, synergistic system demonstrated a surprising level of capability to detect the use of steganographic tools for which the system had no previous exposure.
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